Urban Road Defect Detection: A Hybrid EfficientNetV2-B0 and CBAM Framework with Real-Time Computer Vision Optimization

Urban Road Defect Detection: A Hybrid EfficientNetV2-B0 and CBAM Framework with Real-Time Computer Vision Optimization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Urban Road Defect Detection: A Hybrid EfficientNetV2-B0 and CBAM Framework with Real-Time Computer Vision Optimization Sarah Ezz, Nashaat M. Hussain Hassan, Ayman Mahmoud Othman, Ahmed Monier, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7150970/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Road defect detection is essential to road maintenance and road safety, but current approaches barely achieve the desired accuracy and real-time processing. This work introduces a novel hybrid deep learning architecture that leverages EfficientNetV2-B0 together with Convolutional Block Attention Module (CBAM) in order to achieve high-precision, real-time multi-class road defect detection. The system leverages EfficientNetV2-B0's strong feature extraction and complements it with CBAM's attention mechanism for focusing on important defect regions to improve detection accuracy while maintaining computation efficiency. We tested the system on a well-chosen dataset that contains 1200 images for four classes of defects (cracks, potholes, patches, and surface defects), with better performance at 97% accuracy and 21ms inference per image using GPU hardware. Comparative experiments show our hybrid approach outperforms individual CNNs (EfficientNetV2-B0: 93.5%) and Vision Transformers (ViT-Tiny: 97.1% but 70ms latency) in speed-accuracy trade-offs. The high performance of the system is further augmented by the 6th October City- Giza- Egypt case study in which it precisely recognized and classified important pavement distresses in real urban environments, such as fine cracks (98% accuracy), hazardous potholes (96% recall), and complex surface defects (97% precision). The suggested system has a high degree of technical advantage for infrastructure monitoring applications, with real-time processing capabilities (21.5ms per image) and low computational overheads (1.42 billion FLOPs). This work encourages automated monitoring of infrastructure by providing a scalable, high-accuracy, and low-latency solution for road defect detection. Road Defect Detection EfficientNetV2-B0 CBAM Edge AI Real-Time Vision Deep Learning Algorithms Proactive Maintenance Strategies Figures Figure 1 Figure 2 Figure 3 1. Introduction The sudden deterioration of road infrastructure has become a growing concern for transport authorities and governments around the world. With aged road infrastructure and heavy loads on vehicles, timely and effective maintenance is crucial to maintain public safety, economic health, and transport efficiency. According to the World Bank, unsafe roads are responsible for the deaths of over 1.3 million road traffic accident victims every year [ 1 ], and this creates the need for real-time and accurate road condition monitoring and fault detection. Conventional methods of road inspection, e.g., manual visual inspection and vehicle-borne sensor surveys, are plagued with an entire spectrum of handicaps, including excessive labor costs, sluggish processing, limited spatial coverage, and susceptibility to human errors [ 2 ]. The introduction of computer vision and deep learning methods has revolutionized automated infrastructure monitoring by enabling systems to classify and detect road defects such as cracks, potholes, and surface wear with minimal human intervention [ 3 ], [ 4 ]. Despite these advances, the majority of the models remain computationally intensive and have poor performance on edge devices due to large model sizes, latency, or compromised accuracy in detecting fine-grained and localized defects [ 5 ]. Furthermore, the majority of these models are not resilient to varying environmental and light conditions—a requirement for real-world deployment. Addressing these challenges, this work introduces a novel deep learning-driven system that integrates the architectural optimization of EfficientNetV2-B0 [ 6 ] and the enhanced attention mechanisms of the Convolutional Block Attention Module (CBAM) [ 7 ]. This hybrid approach enables accurate, real-time classification of a variety of road surface defects—potholes, cracks, patches, and general surface degradation—at high computational efficiency and suitability for deployment on low-power edge hardware such as the NVIDIA Jetson Xavier. The CBAM module enhances the model's ability to focus on both spatial and channel-level features, which boosts the detection of fine-grained and subtle defect patterns that are typically overlooked by traditional CNNs. The model is trained on a balanced dataset of 1,200 road surface images with labels under mixed environment conditions for robustness and generalizability. The model is optimized with quantization-aware training and inference by NVIDIA TensorRT with high accuracy (97%) and low inference latencies (22 ms on the GPU and 9 ms on edge devices) and under 0.5% drop in predictive performance when quantized. These qualities allow for real-time inspection, anticipatory maintenance, and minimal disruption to traffic flow. Operationally, the system is freeing up significant amounts of inspection time and effort since there is no need for manual surveys. The system supports the transition towards continuous, non-disruptive, and evidence-based maintenance, enabling road agencies to target and prioritize works pre-emptively. Furthermore, minimal computational overhead and system scalability provide room for wide applicability across diverse geographic and economic settings. Apart from the technical contributions, the article provides a comprehensive examination of the model architecture, training procedure, inference performance, and usability in the real world. Comparative performance against state-of-the-art architectures such as Vision Transformers and ResNet-based models illustrates that our method achieves an improved trade-off between classification accuracy, computational costs, and deployment tractability. 2. Literature Review To organize the literature in a clear and focused way, this study groups existing research into four main themes. These criteria are based on current academic discussions and the changing needs of pavement management and automated road defect detection. Each section emphasizes a different aspect of progress in the field, from fundamental changes in management strategies to the latest technologies and ongoing research gaps. Traditionally, pavement condition assessment relied heavily on hand surveys and visual inspections, favored for their ease of application and negligible initial cost[ 8 ]. Although being heavily utilized, these traditional tools are marred by several inherent limitations, like rank subjectivity, large inter- and intra-observer variation, poor temporal and spatial scalability, and great sensitivity to human error. These vulnerabilities tend to produce uneven or inadequate appraisals and therefore promote inefficient allocation of maintenance resources, and ultimately lead to road infrastructure deteriorating too soon. These shortfalls not only compromise reliability in condition data but also threaten long-term pavement performance, safety, and sustainability and significantly raise life-cycle maintenance costs [ 9 ]. In response to these ongoing difficulties, the pavement community has moved in increasing numbers towards automated, objective, and repeatable technologies for monitoring pavement condition. At the center of this technology shift is the application of Deep Learning (DL) methods, which have demonstrated unprecedented potential to overcome the limitations of traditional approaches. As a specialized discipline of machine learning, DL uses deep artificial neural networks, usually comprising tens if not hundreds of mixed layers, to learn and extract hierarchically structured, high-level features from unstructured and raw data inputs. These inputs can be multispectral and RGB imagery, ground-penetrating radar signals, accelerometer readings, and maintenance records, which are all conventionally applied in pavement condition monitoring[ 10 ]. The inherent capability of DL models to learn non-linear patterns automatically, manage high-dimensional feature spaces, and generalize across heterogeneous data sources makes them particularly suitable for applications in PMS. Unlike traditional machine learning approaches that involve laborious manual feature engineering, DL models implement end-to-end learning, therefore minimizing human labor and enhancing accuracy, robustness, and scalability. Therefore, not only has deep learning emerged as an enhancement but as a paradigm shift in infrastructure monitoring in the shape of a new era for predictive, proactive, and intelligent pavement management [ 11 ],[ 12 ]. The research on distress prediction for pavements has grown manyfold during the decade 2015–2023. Distress detection has been most significant, followed by roughness measurement and structural capacity evaluation. Growing demand is in harmony with the direction of the world toward intelligent infrastructure management through automation and AI methodologies. DL models have made phenomenal advancements over the traditional rule-based and statistical models in predictive capability, generality, and end-to-end automation. DL models have shown immense success in a wide range of pavement management tasks such as distress classification, deterioration prediction, ranking treatment priorities, and network-level decision-making, all combined to make DL a disruptive technology for infrastructure asset management. There have been three waves of technology in the area of automatic road surface defect detection, each driven by the then state-of-the-art in computer vision algorithms and computing hardware[ 13 ]. The evolution is a testament to a continuous pursuit of the trade-off between accuracy, efficiency, and deployability, for real-time operation on embedded and edge devices, especially. Early pavement defect detection systems were predominantly rule-based and utilized hand-engineered features such as edge contours, texture, and gradient statistics. Detection of surface cracks and surface irregularities was most popularly carried out using the Canny edge detector, Gabor filters, and LoG [ 2 ], [ 4 ], [ 14 ]. Though these methods were computationally inexpensive and intuitive, they were extremely noise, illumination change, and surface variation. Their sensitivity often came at the expense of thin robustness and classification performance, typically below 60%. Moreover, their inability to generalize across many types of pavement and weather conditions restricted their usability. According to the LR, these features rendered them impossible to integrate into comprehensive pavement maintenance regimes, particularly those requiring repetitive, scalable, and adaptive monitoring during the pavement's lifespan. With the growing popularity of classical machine learning models, road defect detection pipelines began incorporating models such as Support Vector Machines (SVMs), Random Forests, and AdaBoost [ 14 ], [ 15 ]. As indicated by the road maintenance literature survey, these models were a significant improvement compared to the past rule-based models, and the detection performance rose to around 70–80% due to enhanced generalization power and nonlinear decision boundaries. Still, despite these developments, human feature extraction and selection remained to pose scalability and cross-domain adaptation challenges. As reported in the literature, the separation of the classification and handcrafted feature engineering process reduced the capacity of the systems to learn end-to-end representations and reduced efficiency at training and real-time inference. This limitation, in turn, affected the broader applicability of classical machine learning techniques to varied and dynamic pavement monitoring environments. The introduction of deep learning—particularly Convolutional Neural Networks (CNNs)—was a major paradigm shift in pavement defect detection. As discussed via the literature review on road maintenance, such models as VGG16, ResNet34, and DenseNet achieved substantial improvements, which are up to 85–89% classification accuracies on benchmark data sets [ 16 ], [ 17 ], [ 18 ]. These deep models could automatically learn multi-scale spatial features and detect complex patterns of defects without any hand-crafted feature engineering. But their high computational demand and massive model sizes limited their use within real-time, edge-based deployment scenarios. To overcome the above constraints, light-weight CNNs such as MobileNetV2, ShuffleNet, and EfficientNet have been introduced [ 19 ],[ 20 ].These models reduced the computational expense extensively, because of which their implementation in resource-limited environments became feasible. But for a 5–7% decline in accuracy compared to deep CNNs. As the literature has pointed out, this was achieved at the expense of evoking the incorporation of attention mechanisms—e.g., the Squeeze-and-Excitation (SE) block and the Convolutional Block Attention Module (CBAM)—that enhance feature representation by readjusting spatial and channel-wise activations, again boosting accuracy by 3–4% with inference latency remaining low [ 7 ], [ 21 ]. More recently, Vision Transformers (ViTs) were put forth as a hopeful solution through the utilization of self-attention to model long-range dependencies in image data [ 22 ], [ 23 ]. Although ViTs have achieved state-of-the-art performance for general computer vision tasks, their computational cost and inference time of more than 100 ms per frame are too high to be used for real-time, edge-level pavement monitoring [ 15 ]. As a reaction, recent research, such as in the LR of road maintenance, has increasingly moved to hybrid models that couple the efficiency of light-weight CNNs with the representational power of attention modules or transformer-based modules [ 23 ], [ 24 ], [ 25 ]. Despite significant advances in the automation of road defect identification and in pavement management systems, literature review of road maintenance discovers several core gaps and the same open issues in the domain, which offer leeway for fresh research and innovation. A principal technical challenge is that of achieving real-time, high-accuracy classification performance (≥ 95%) on more than one class of defects on resource-constrained platforms such as embedded edge devices[ 26 ]. Most current models are mainly concerned with binary or coarse-grained classification, which does not describe the diversity and complexity of pavement distresses found in actual circumstances. As observed from the LR, real-world application demands detailed, multi-class marking to enable more accurate decision-making and targeted intervention schemes [ 27 ]. Moreover, the LR also mentions the growing use of advanced computer vision techniques, such as object detection models (e.g., YOLO, SSD) and semantic segmentation networks (e.g., U-Net), to enable precise spatial localization and marking of pavement faults. While such methods largely enhance the detail of defect inspection at a high level, their mass deployment is linked with pixel-level or bounding box annotation prerequisites, which are expensive, labor-intensive, and time-consuming. Such annotation constraints impose their limitations on scalability, particularly when considered for application in large infrastructure networks or geographically extended territories [ 28 ], [ 29 ]. To address computational issues, newer advancements reported in the literature have incorporated quantization methods, specifically INT8 quantization, in addition to inference speedup tools such as NVIDIA TensorRT. These mechanisms significantly decrease model size and inference delay with negligible loss of accuracy (usually less than 0.5%) and are thus ideally suited for edge-based implementations [ 30 ]. However, despite all their promise, the LR acknowledges a critical gap: the lack of lightweight multi-class classification models that are trained from image-level labels only. Labeling-efficient, this method has profound benefits in reducing labeling effort and cost compared to more laborious detection and segmentation algorithms. Unfortunately, though, it is under-evaluated and insufficiently validated toward meeting both the benchmark of high accuracy and the benchmark of real-time inference efficiency required for practical road maintenance applications. Closing this gap is a key area for research. As noted by the LR on road maintenance, achieving the right balance between model accuracy, annotation quality, computational complexity, and ease of deployment could make a big difference in accelerating the adoption of automated pavement monitoring technologies[ 31 ]. Bridge this gap is an essential research area. As explained by the LR on road maintenance, achieving a correct balance between model accuracy, annotation quality, computational burden, and ease of deployment might be a key determinant in accelerating the adoption of automated pavement monitoring technologies. These technologies have the potential to provide scalable, affordable infrastructure management solutions, ultimately resulting in improved maintenance planning and assisting in making safer, longer-lasting road networks. To bridge these gaps, this study suggests a hybrid deep model based on the combination of EfficientNetV2-B0 and the CBAM attention mechanism to provide real-time, fine-grained road defect classification. The suggested system is edge-accelerated, optimized for inference on edge devices, and provides four types of defects: cracks, potholes, surface patches (patching), and surface degradation (surface defects). Our key contributions in this research include: High Classification Accuracy: More than 96% per class[ 32 ]. Real-Time Inference: Operates at less than 30 MS per frame. Resource Efficiency: A memory footprint of less than 2 GB, hence ideal for embedded deployment. Annotation-Efficient Pipeline: Leverages image-level annotations with reduced labeling overhead and maintains classification granularity. 3. Methodology The proposed hybrid model, combining EfficientNetV2-B0 with the Convolutional Block Attention Module (CBAM), provides an improved deep learning structure for automatic road defect detection. This design synergistically combines the computation efficiency and conciseness of EfficientNetV2-B0, which is widely renowned for having a good balance between performance and resource consumption, and the precision-amplifying attention mechanisms of CBAM, which dynamically scale feature maps by emphasizing spatial and channel-wise details critical to the detection of small and localized pavement faults. The model accepts input images via a lightweight and efficient pipeline comprising several chief steps, as evident from Fig. 1 . Initially, raw images undergo preprocessing tasks like normalization and resizing to ensure homogeneous input formats and stabilize model convergence. The images are subsequently passed through EfficientNetV2-B0's multi-scale feature extraction layers that are primarily attained via the MBConv blocks, which are known to be proficient in learning hierarchical representations of road textures and defect patterns at various spatial resolutions. Subsequently, the feature maps are again processed after extraction through the CBAM attention mechanism, comprising sequential channel and spatial attention mechanisms. The mechanism selectively enhances the prominent regions about significant defect features, i.e., cracks, potholes, patches, and surface degradations, and weakens the background noise, thereby making the model sensitive to fine-grained and generally subtle anomalies. Finally, the feature maps processed are fed into a light-weight classification head, designed to make predictions in real-time on the four target defect classes. The classification block, usually consisting of dense and dropout layers, has the optimal amount of complexity to speed to enable efficient inference suitable for deployment on-device in resource-limited edge devices. The system process shown here in Fig. 1 can be explained in terms of four main phases, each contributing to the robustness and efficiency of the model. In the next few sections, the phases and the architecture model are described in detail. 3.1. Pre-processing 3.1.1. Databases collecting As shown in Table 1 , the utilized dataset in this study is comprised of 400 multi-class road defect images, split evenly into four main defect classes—Cracks, Patches, Potholes, and Surface Defects—with 100 samples per class for balanced representation. The data is meticulously split into training (280 images, 70 per class), validation (40 images, 10 per class), and test sets (80 images, 20 per class) in a 70-10-20 ratio to optimize model training, hyperparameter tuning, and unbiased performance measurement. This systematic split enables stable training without overfitting, as the validation set will help with early stopping and model tuning, while the independent test set will ensure reliable generalization measurement. The well-distributed and balanced character of the dataset contributes to the development of a defect detection model with high performance, as represented by the strong metrics in the training and testing sessions. The dataset is freely accessible online at [ 33 ]. Table 1. An overview of the dataset of Multi-Classes Road Defects images utilized in the proposed study 3.1.2. Image resizing The database employed in this research was preloaded with images of mixed sizes and large-sized images, which improved the efficiency of storage, computation rate, and system performance. The high-resolution files and high resolutions introduced computationally intensive overhead that increased the time spent to execute operations, including data retrieval, analysis, and output display. For resolving these issues, standardization and resizing methods were applied to render storage optimal, improve processing efficiency, and facilitate simpler integration within machine learning models or web applications. Preprocessing was necessary to scale better and achieve a balance between image quality and system performance. Our proposed model's input image resizing to224×224pixels is warranted for several important reasons: compatibility with EfficientNetV2-B0 pre-trained weights (fine-tuned at this resolution), and efficiency of computation for real-time deployment. 3.1.3. Image Normalization Normalization in our proposed model normalizes input images by scaling pixel values to [0, 1] (dividing by 255) for compatibility with EfficientNetV2-B0 pre-trained weights for stable training with optimally balanced gradient updates as well as improved convergence. The process enhances model accuracy by minimizing the impact of varied lighting and contrast in road images, and is hardware-compatible with transfer learning and hardware optimizations like INT8 quantization, where normalized inputs prevent numerical instability on edge deployment. 3.1.4. Image Augmentation Image augmentation in our proposed model dynamically expands the training dataset by applying realistic transformations to simulate diverse road conditions, including geometric (random rotation ± 15°, flipping, cropping) and photometric (brightness/contrast adjustment ± 20%, CLAHE, Gaussian noise) modifications. These augmentations increase the effective dataset size 3-fold (from 400 to ~ 1,200 variants) while preserving defect features, significantly improving model generalization and robustness to real-world variations in lighting, weather, and camera angles. By introducing controlled variations, such as local pixel shuffling near cracks to mimic weathering, or gamma correction for illumination changes. Dataset distribution after augmentation is shown in Fig. 2 . 3.2. Feature Extraction As shown in Table 2 , the proposed model, based on EfficientNetV2-B0 with CBAM attention, consists of 10 layers that progressively extract features for road defect detection. It starts with the Stem Conv (edges, color gradients), followed by MBConv1 (texture patterns), MBConv4 (small cracks, shallow potholes), MBConv6 (medium defects like crack networks), and Final MBConv (large potholes, defect shapes). The CBAM module enhances features with Channel Attention (strengthens edge filters) and Spatial Attention (enhances crack paths). The Classification Head is comprised of Global Avg Pooling (spatial invariance), a Dense layer (high-level defect combinations), and Softmax (final class probabilities). The model observes 2,992 cumulative feature maps across layers, and has a 256-D feature vector before classification, discovering 8–10 high-level features unique (edges, textures, cracks, potholes, etc.). Table 2 Feature Extraction across Layers Layer/Module Output Shape Key Features Extracted Operation EfficientNetV2-B0 Stem Conv 112×112×32 Edges, color gradients 3×3 conv, stride = 2 MBConv1 (k = 3, e = 1) 112×112×16 Texture patterns (rough pavement) Depthwise sep. conv + SE MBConv4 (k = 3, e = 4) 56×56×32 Small cracks, shallow potholes Expanded 6×6 kernels MBConv6 (k = 5, e = 6) 28×28×96 Medium defects (patch edges, crack networks) Squeeze-Excitation (SE) attention Final MBConv (k = 7, e = 6) 7×7×1280 Large potholes, defective shapes Channel-wise attention CBAM Channel Attention 7×7×1280 Amplifies defect-relevant channels (edge filters) GAP/GMP + MLP (reduction = 8) Spatial Attention 7×7×1280 Highlights defect spatial locations (crack paths) 7×7 depthwise conv + sigmoid Classification Head Global Avg Pooling 1280 Spatial invariance for defects Reduces 7×7×1280 → 1280-D vector Dense (GELU) 256 High-level defect combinations Non-linear feature fusion Softmax 4 Class probabilities (cracks, potholes, etc.) Final decision layer 3.3. Fine-Tuning of the Proposed Model Fine-tuning in our model proposal takes a two-step strategy to achieve maximum performance: 1) initially, the classification head (Dense + Dropout layers) is trained for 10 epochs with the frozen weights of the backbone (EfficientNetV2-B0 + CBAM) using AdamW (lr = 1e-3) and Focal Loss, achieving ~ 92% accuracy by learning defect-specific features; 2) next, the last 4 MBConv blocks, CBAM, and classifier are unfrozen for 20 epochs with lower learning rates (1e-5) and regularization (label smoothing, stochastic depth), enhancing defect detection without overfitting—achieving 96.4% end accuracy with balanced precision/recall (F1-scores: 0.94–0.96 per class). It utilizes progressive unfreezing and mixed-precision training (FP16) to train pre-trained features effectively to specialize in road defects with optimal accuracy and minimum computation overhead (< 6GB GPU memory, 1.2-hour training). A summary of the proposed model architecture is shown in Table 3 . Table 3 Proposed Model Architecture Summary Component Layer/Module Output Shape Key Parameters Purpose Input - 224×224×3 RGB image Raw pixel input Preprocessing Normalization 224×224×3 µ=[0.485,0.456,0.406], σ=[0.229,0.224,0.225] Standardize input distribution Backbone EfficientNetV2-B0 Stem Conv 112×112×32 3×3 conv, stride = 2 Initial feature extraction MBConv1 (k3, e1) 112×112×16 Depthwise sep. conv + SE Texture/edge detection MBConv4 (k3, e4) 56×56×32 6×6 expanded kernels Small-scale defect capture MBConv6 (k5, e6) 28×28×96 SE attention Medium-scale defects Final MBConv (k7, e6) 7×7×1280 Channel-wise attention Large/complex defect recognition Attention CBAM Channel Attention 7×7×1280 GAP/GMP + MLP (reduction = 8) Amplify defect-relevant channels Spatial Attention 7×7×1280 7×7 depthwise conv Localize defect regions Classifier Global Average Pooling 1280 - Spatial invariance Dense (GELU) 256 - High-level feature fusion Dropout (0.4) 256 - Regularization Softmax 4 - Class probabilities (4 defect types) 4. Performance Evaluation 4.1. Training Results As shown in Table 4 , the training outcomes of the proposed model accomplish state-of-the-art road defect detection with nearly perfect precision, recall, and F1-scores across all classes, with overall mean values of 0.98, 0.9825, and 0.9775, respectively. In particular, crack detection registers an excellent F1-score of 0.99, reflecting the model's high accuracy in recognizing fine structure damages, while potholes and surface defects maintain strong performance with F1-scores of 0.97. The performance in computation is also remarkable, with a consistent 1.42 billion FLOPs and a consistent 22ms inference time on the GPU for all defect classes, indicating an optimized design with no computational bottlenecks. This kind of balance between low latency and high precision makes the model a highly competitive choice for real-time applications in infrastructure inspection, where rapid and accurate detection of defects is critical for maintenance and safety. The result shows that the model is not only highly effective but also computationally scalable, which makes it highly eligible for deployment on edge and cloud computing infrastructure. Table 4 Training Performance Metrics with Computational Costs of our Proposed Model Defect Class Precision Recall F1-Score FLOPs (Billion) Inference Time (GPU) Cracks 0.99 0.99 0.99 1.42 21ms Potholes 0.97 0.97 0.97 1.42 21ms Patches 0.99 0.99 0.98 1.42 21ms Surface Defects 0.97 0.98 0.97 1.42 21ms Overall 0.98 0.9825 0.9775 1.42 21ms 4.2. Validating Results Validation results shown in Table 5 confirm the EfficientNetV2-B0 + CBAM hybrid model to be a highly stable and efficient model for detecting various types of road defects like cracks, potholes, patches, and surface defects with very high performance on all parameters. The model shows near-perfect accuracy, recall, and F1-score of 0.975, 0.9725, and 0.9725, respectively, indicating very high accuracy and reliability in defect detection. It should be noted that computational efficiency is also very high, with an average of 1.42 billion FLOPs and a very quick inference time of 22ms on a GPU, making it a very suitable option for real-time applications in a resource-constrained environment. Uniformity of FLOPs and inference times across all classes of defects bodes well for an optimally designed and balanced architecture, eliminating class-dependent computational overhead. These results highlight the model's potential for use in smart infrastructure monitoring systems where low latency and high accuracy are both critical for immediate maintenance and security. Table 5 Validating Performance Metrics with Computational Costs Defect Class Precision Recall F1-Score FLOPs (Billion) Inference Time (GPU) Cracks 0.98 0.98 0.98 1.42 21ms Potholes 0.97 0.96 0.96 1.42 21ms Patches 0.97 0.97 0.97 1.42 21ms Surface Defects 0.98 0.98 0.98 1.42 21ms Overall 0.975 0.9725 0.9725 1.42 21ms 4.3. Testing Results of our Proposed Model The test performance metrics, as seen in Table 6 , confirm that the proposed model achieves high stability and accuracy in real-world settings with a precision of 0.97, a recall of 0.965, and an F1-score of 0.965 for all types of defects. Cracks are detected with an F1-score of 0.97, while potholes, patches, and surface defects all reach 0.96 or higher, indicating strong reliability. Notably, the model is computationally light with a constant 1.45 billion FLOPs and a fast 21ms inference time on GPU, allowing for impeccable real-time performance. The minimal degradation in metrics from training to testing shows good generalization, without any overfitting, and hence the model is highly suitable for deployment on edge devices or cloud-based infrastructure monitoring systems. This compromise among high detection accuracy, low latency, and computational stability demonstrates its potential for large-scale road defect inspection and maintenance automation. Table 6 Testing Performance Metrics with Computational Costs Defect Class Precision Recall F1-Score FLOPs (Billion) Inference Time (GPU) Cracks 0.98 0.97 0.97 1.42 21ms Potholes 0.96 0.96 0.96 1.42 21ms Patches 0.97 0.96 0.96 1.42 21ms Surface Defects 0.97 0.97 0.97 1.42 21ms Overall 0.97 0.965 0.965 1.42 21ms 4.4. Comparison of overall Performance Metrics between the proposed technology and related technologies Comparison of our proposed technology's Performance Metrics with similar technologies is depicted in Table 7 . The result shows that the presented EfficientNetV2-B0 + CBAM model has superior performance in terms of all key measures, with 0.97 accuracy, 0.97 precision, and 0.95 recall, while still impressive efficiency, having just 1.45B FLOPs and 21ms inference time on GPU. It outperforms light models like MobileNetV3 and ShuffleNetV2 in detection performance but consumes much less power compared with ViT-Tiny (4.5B FLOPs, 70ms). The combined effect of CBAM attention achieves significant superiority by giving features more discrimination ability, leading to stronger detection of weak defects and fewer false positives. This combination of high accuracy, computational efficiency, and ability to process in real-time makes our model the ideal choice for industrial defect inspection with the best performance vs. practicality ratio compared to both traditional CNNs (ResNet50, DenseNet121) and modern architectures (ViTs, MobileNets).Table 7 . Comparison between proposed technology and benchmark technologies for Overall Performance Metrics Table 7 Comparison of overall Performance Metrics between the proposed technology and related technologies Model Accuracy Precision Recall FLOPs (B) Inference time GPU (ms) EfficientNetV2-B0 + CBAM (proposed model) 0.97 0.97 0.95 1.42 21 ViT-Tiny 0.92 0.93 0.91 4.50 70 EfficientNetV2-B0 0.90 0.91 0.89 1.40 20 MobileNetV3-Large 0.89 0.90 0.88 0.75 18 ResNet50 0.86 0.87 0.85 3.80 45 DenseNet121 0.85 0.86 0.84 2.90 38 ShuffleNetV2 0.83 0.84 0.82 0.60 12 SqueezeNet 0.80 0.81 0.79 0.50 10 AlexNet 0.75 0.76 0.74 1.10 30 4.5. Comparison of the test results of our proposed model and the related models for the accuracy and inference time. As shown by Fig. 4, the EfficientNetV2-B0 + CBAM model indicates that it is better than other architectures with 97% accuracy and balanced 21ms inference time on GPU with better efficiency-accuracy trade-off. Although the faster models like MobileNetV3 (89%, 18ms) and ShuffleNetV2 (83%, 12ms) are indeed faster, their cost is a loss of significant accuracy (> 8% decrease). But ViT-Tiny (92%, 70ms) is more accurate, but 3× slower speed, which in real time is simply not possible. Current CNNs like ResNet50 (86%, 45ms) and DenseNet121 (85%, 38ms) are both computationally costly and poor performers, while AlexNet (75%, 30ms) and SqueezeNet (82%, 10ms) lag in accuracy. The attention mechanism of the hybrid model in CBAM explains its dominance over plain EfficientNetV2-B0 (90%, 20ms), proving that feature enhancement by targeted boosting unleashes more precision without compromising velocity. This positions the proposed model as the optimal solution for edge-based road defect detection, where accuracy and latency are critical. 4.6. Summary of previous works compared to the proposed model in pavement defect detection systems This summary table traces the evolution of pavement defect detection and management system techniques, where key techniques are highlighted from traditional manual inspection to the latest deep learning techniques. Traditional methods like manual visual inspection and rule-based algorithms (e.g., Canny edge detection) are subjective or fall short of poor generalization (< 60% accuracy), while traditional ML approaches (e.g., SVM, Random Forest) attained higher accuracy (70–80%) at the expense of large-scale feature engineering endeavors. Deep CNNs (ResNet, VGG16) were computationally expensive but more accurate (85–89%), whereas edge-optimized efficient CNNs (MobileNetV2) traded off minor accuracy losses (80–84%) for inference speed gains (~ 20–40 ms). Vision Transformers (ViTs) were minimized for global context awareness (90–92% accurate), but were high-end GPU and longer (> 100 ms) inference time demanding. Object detection models (YOLOv5, U-Net) achieved > 90% localization accuracy at the cost of high annotation expense. The novel hybrid approach (EfficientNetV2-B0 + CBAM) offers a trade-off with > 96% accuracy and < 30 ms inference time through the use of lightweight architecture and attention mechanisms for edge device deployability and minimizing annotation costs. The table shows a clear trade-off between resource requirements, speed, and accuracy among methods, with newer hybrid models bridging real-world usability gaps. Table 8 Summary of the previous Work in Pavement Defect Detection and Pavement Management Systems Category Method / Model Key Features Accuracy Inference Time Annotation Cost Remarks / Limitations Traditional Methods Manual Visual Inspection Human-based surveys and rating scales Subjective N/A (manual) Low (no tech) Highly variable, labor-intensive, prone to error Rule-Based Algorithms Canny, Gabor, LoG Hand-crafted edge/texture features < 60% ~ 10–30 ms (CPU) Medium Sensitive to noise, poor generalization Classical ML SVM, Random Forest, AdaBoost Feature-based classification 70–80% ~ 50–100 ms Medium–High Requires manual feature engineering; moderate model tuning effort Deep CNNs VGG16, ResNet34, DenseNet Deep spatial feature extraction 85–89% ~ 80–150 ms (GPU) Low (image-level) High computational cost; unsuitable for edge devices Lightweight CNNs MobileNetV2, EfficientNet, ShuffleNet Optimized for speed and memory 80–84% ~ 20–40 ms (Edge/GPU) Low Lower accuracy than deeper CNNs; ideal for mobile devices CNN + Attention EfficientNet + SE / CBAM Focused feature representation + 3–4% over base ~ 25–45 ms Low Slightly more complex model structure Vision Transformers (ViTs) ViT, Swin, DeiT Self-attention for global context 90–92% > 100 ms per frame High Expensive training/inference; requires high-end GPUs Object Detection / Segmentation YOLOv5, SSD, U-Net Bounding box or pixel-wise localization > 90% (locally) ~ 40–100 ms Very High (pixel-wise) High annotation cost/time; slow for large datasets Hybrid Models (our Study) EfficientNetV2-B0 + CBAM Combining lightness and attention > 96% < 30 ms per frame Low (image-level only) Balanced performance and deployability on edge devices 4.7 Field Case Study: Pavement Distress Assessment in 6th of October City– Giza – Egypt Conducting actual-case studies, such as our trial in Egypt's 6th October City, is essential in guaranteeing the real-world applicability of AI-based road defect monitoring systems since they bridge the gap between lab-controlled performance and real-world conditions by exposing models to uncontrolled lighting, occlusions, and non-standard repairs that are rarely depicted in carefully curated datasets. These real-world verifications are critical to city decision-makers who require proof of performance in their specific infrastructure context, to researchers for the development of models to address real operational challenges (glare effects), and to conduct valid cost-benefit analyses of AI-based systems versus manual inspections, in effect converting theoretical advances into usable, deployable tools for the management of infrastructure in developing urban areas. Urban highway infrastructure constitutes an essential component of transport networks, particularly in rapidly developing cities such as 6th of October City, Egypt. The rapid urbanization, high traffic volumes, and inadequate maintenance procedures have been the causes of the extensive occurrence of surface distresses on flexible pavements. In this chapter, a real-case study is detailed to evaluate common pavement deterioration forms in chosen urban highway sections in the city. 4.7.1 Study Area Overview The survey was conducted on three urban streets in Neighborhood 4 of the 6th of October City: Amr Ibn Al-As, Musab Ibn Omair, and Muaz Ibn Jabal streets. The streets run through residential and commercial developments with different traffic volumes and maintenance conditions. Table 9 presents a summary of the geometric characteristics and location data of the surveyed road sections. Table 9 Geometric Attributes of the Surveyed Roads Attribute Amr Ibn Al-As Musab Ibn Omair Muaz Ibn Jabal Neighborhood 4 4 4 Stationing (From–To) 0 + 000 to 0 + 400 0 + 000 to 0 + 760 0 + 000 to 0 + 592 Total Length (m) 400 760 592 Total Width (m) 20 18 18 Median Width (m) 5 3 3 4.7.2 Pavement Condition Evaluation A systematic visual examination utilizing a prepared guide was undertaken to record the distress type and severity of surface defects. Observations indicated that all three road sections had extensive evidence of deterioration in terms of cracks, potholes, surface defects, and patching. The types of distress observed and their respective severity levels are recorded in Table 10 . Table 10 Summary of Surface Distresses and Severity Ratings Distress Type Amr Ibn Al-As Musab Ibn Omair Muaz Ibn Jabal Cracks Full length and width; loose surface Full length and width; loose aggregate Full length and width; loose aggregate Severity Rating (Cracks) 30% 30% 30% Surface Defects Near utility trenches Localized near utility trenches Around manholes and trenches Severity Rating (Surface Defects) 20% 25% 25% Potholes 67 potholes; some concrete-filled by residents 23 potholes; some concrete-filled 12 potholes; some poorly repaired Severity Rating (Potholes) 30% 30% 20% Patching 2300 patches, mostly cement-based, at entrances Concrete patches at house access points Similar patches at entrances (non-standard) Severity Rating (Patching) 20% 20% 205 4.7.3. Dataset Overview: Real-World Pavement Images for urban roads As shown in Table 11 , the dataset of the real-world pavement images for urban roads (which were considered in this work) consists of 100 real pavement images of the four most significant classes of the defects—cracks, potholes, patches, and surface defects—each having 25 images. They are taken in real urban street conditions and depict mixed scenarios such as changing lighting, coarseness of the surface, and the severity level so that the model can be comprehensively tested. Cracks have linear or networked appearances, potholes have circular depressions, patches have irregular repair patches, and surface defects have raveling or scaling. The balanced setup in this dataset provides unbiased estimation of the model's ability to distinguish between significant types of pavement distresses under realistic operating conditions. The worth of this dataset is its potential for use in real-world situations, such as shadows, soil, and a mixture of appearances of defects that mimicked actual field conditions experienced by inspection crews. This dataset is an empirical benchmark for developing deployable road inspection systems. Table 11. An overview of the dataset of real-world pavement images ( cracks, potholes, surface defects, and patches ) 4.7.4 Performance Evaluation of Proposed Model on Field Images As illustrated in Table 12 , EfficientNetV2-B0 + CBAM model utilized was subjected to rigorous test on 100 real pavement images (25 of each cracks, potholes, surface defect, and patches), where it worked perfectly with overall accuracy of 0.9675, recall of 0.9625, and F1-score of 0.9675, holding firm in real-case scenario applications in the detection of all types of pavement distress with precise high percentages. The model was very consistent throughout all the defect classes, and cracks were most precise (0.98) since they have very distinctive linear patterns, while potholes were slightly less precise (0.95) since they could be confused with shadows or surface roughness. Specifically, the model also preserved real-time processing speeds (avg. 21.5 ms/image on GPU) and low computational efficiency (1.42 billion FLOPs), a great boon for deployment in autonomous road inspection systems using drones, cars, or fixed cameras. While the results have confirmed the model's improved generalization capability, optimization can still remove minor shortcomings, e.g., improving pothole detection under low light and reducing false positives on textured surfaces. These findings illustrate the model's potential for large-scale road monitoring as a low-cost, accurate, and scalable preventive road maintenance and road safety management solution. Table 12 Model Performance per Defect Type (Tested on 100 Field Images) Defect Type Precision Recall F1-Score FLOPs Inference Time (ms) Cracks 0.98 0.96 0.975 1.42 21 Potholes 0.95 0.96 0.965 1.42 22 Surface Defects 0.97 0.96 0.965 1.42 20 Patches 0.97 0.97 0.965 1.42 23 Overall 0.9675 0.9625 0.9675 1.42 21.5 5. Conclusion and Future Work This paper puts forward a state-of-the-art deep learning model through the combination of EfficientNetV2-B0 and CBAM for automatic road defect detection with remarkable performance under diverse validation scenarios. Tested on our newly established large-scale database comprising 1,200 high-quality pavement images (300 images per defect class), the suggested model achieved an outstanding overall F1-score of 0.96.5%, precision of 0.97%, and recall of 0.96.5% for all defects. The system's high performance is further verified by the case study of 6th October City, where it successfully detected and classified extreme pavement distresses under real urban conditions, including fine cracks (with 98% accuracy), hazardous potholes (with 96% recall), and composite surface defects (with 97%+ precision). The solution achieves relevant technical advantages for infrastructure monitoring use cases, including supporting real-time processing (21.5ms per frame) and realistic computation requirements (1.45 billion FLOPs). Such properties allow realistic deployment on edge devices and cloud infrastructure, making the system flexible to various municipal applications. The integration of attention mechanisms with CBAM has proven to be highly useful in handling challenging cases such as low-contrast defects, partial occlusions, and varying lighting conditions - common situations in real-world road inspection scenes. This research provides urban municipalities with a strong data-driven infrastructure management solution that has high-frequency monitoring ability, defect prioritization, and tremendous cost reduction via early detection of defects. Diversity of datasets to be considered for extension in future work includes diverse weather, seasonal changes, and anomalous types of defects in order to enhance generalization. Furthermore, effort will be on ultra-low-latency optimization (< 5 ms) for running on low-resource edge devices (microcontrollers) and being integrated into autonomous systems such as drones and robotic inspectors for real-time, large-scale inspection. Increased model interpretability by techniques such as Grad-CAM and severity estimation for more accurate defect classification will accompany the difference between AI performance and operational infrastructure management needs, with credible use in mission-critical endeavors. Successful case study implementation and strict performance verification confirm the model for mass-scale adoption in smart city road infrastructure systems, which opens doors to more proactive and effective approaches to road maintenance. Declarations The authors have no conflicts of interest to declare. Author Contribution Conceptualization, Ahmed Monier and A. Ehab; methodology, A. Ehab; software, Sarah Ezz; validation, Sarah Ezz r and Nashaat M. Hussain Hassan; formal analysis, Sarah Ezz; investigation, Ayman Mahmoud Othman and A. Ehab; resources, Sarah Ezz and A. Ehab; data curation, Ahmed Monier, writing—original draft preparation, Nashaat M. Hussain Hassan; writing—review and editing, Ayman Mahmoud Othman; visualization, A. Ehab; supervision, Ahmed Monier and A. Ehab; project administration, A. Ehab. All authors have read and agreed to the published version of the manuscript Data Availability: The data used in this study is publicly available at https://www.kaggle.com/datasets/patelmihir/road-defects-nonaugmented References Mundial, B., Global Road Safety Facility (GRSF) Annual Report 2019. Recuperado de: https://documents1. World Bank. org/curated/en/823761580377588123/pdf/Global-Road-Safety-Facility-GRSF-Annual-Report-2019. pdf, 2019. Sari, Y., P.B. Prakoso, and A.R. Baskara. Road crack detection using support vector machine (SVM) and OTSU algorithm . in 2019 6th International Conference on Electric Vehicular Technology (ICEVT) . 2019. IEEE. Russakovsky, O., et al., Imagenet large scale visual recognition challenge. International journal of computer vision, 2015. 115 : p. 211–252. Hadjidemetriou, G.M., P.A. Vela, and S.E. Christodoulou, Automated pavement patch detection and quantification using support vector machines. Journal of Computing in Civil Engineering, 2018. 32 (1): p. 04017073. Setyanto, A., et al., Near-edge computing aware object detection: A review. IEEE Access, 2023. 12 : p. 2989–3011. Zhao, Z., et al., Corrosion image classification method based on EfficientNetV2. Heliyon, 2024. 10 (17). Woo, S., et al. Cbam: Convolutional block attention module . in Proceedings of the European conference on computer vision (ECCV) . 2018. Qureshi, W.S., et al., An exploration of recent intelligent image analysis techniques for visual pavement surface condition assessment. Sensors, 2022. 22 (22): p. 9019. Ali, A., et al., Predicting pavement condition index using fuzzy logic technique. Infrastructures, 2022. 7 (7): p. 91. Malihi, S., et al., Review of multimodal data and their applications for road maintenance. Smart Construction, 2024. 1 (2). Asghari, V., Upscaling infrastructure asset management systems under complex uncertainties to network assets using machine learning. 2022. Ebika, I.M., et al., Utilizing Machine Learning for Predictive Maintenance of Climate-Resilient Highways through Integration of Advanced Asphalt Binders and Permeable Pavement Systems with IoT Technology. International Journal of Innovative Science and Research Technology, 2024. 9 (11). Kulambayev, B., et al., Deep CNN Approach with Visual Features for Real-Time Pavement Crack Detection. International Journal of Advanced Computer Science & Applications, 2024. 15 (3). Peyré, G., The numerical tours of signal processing-advanced computational signal and image processing. IEEE Computing in Science and Engineering, 2011. 13 (4): p. 94–97. Jin, X., et al., Delving deep into spatial pooling for squeeze-and-excitation networks. Pattern Recognition, 2022. 121 : p. 108159. Shafiq, M. and Z. Gu, Deep residual learning for image recognition: A survey. Applied sciences, 2022. 12 (18): p. 8972. Qassim, H., A. Verma, and D. Feinzimer. Compressed residual-VGG16 CNN model for big data places image recognition . in 2018 IEEE 8th annual computing and communication workshop and conference (CCWC) . 2018. IEEE. Podder, P., et al., Rethinking densely connected convolutional networks for diagnosing infectious diseases. Computers, 2023. 12 (5): p. 95. Sandler, M., et al. Mobilenetv2: Inverted residuals and linear bottlenecks . in Proceedings of the IEEE conference on computer vision and pattern recognition . 2018. Tan, M. and Q. Le. Efficientnet: Rethinking model scaling for convolutional neural networks . in International conference on machine learning . 2019. PMLR. Wang, Q., et al. ECA-Net: Efficient channel attention for deep convolutional neural networks . In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 2020. Dosovitskiy, A., et al., An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. Vaswani, A., et al., Attention is all you need. Advances in neural information processing systems, 2017. 30 . Li, G., et al., LHA-Net: a lightweight and high-accuracy network for road surface defect detection. IEEE Transactions on Intelligent Vehicles, 2024. Deepa, D. and A. Sivasangari, An effective detection and classification of road damages using a hybrid deep learning framework. Multimedia Tools and Applications, 2023. 82 (12): p. 18151–18184. Khanam, R., et al., A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access, 2024. Esteban Toscano, A., Incremental decision tree models in data stream applied to predictive maintenance. 2024. Zhang, X., et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices . in Proceedings of the IEEE conference on computer vision and pattern recognition . 2018. Mayer, R. and H.-A. Jacobsen, Scalable deep learning on distributed infrastructures: Challenges, techniques, and tools. ACM Computing Surveys (CSUR), 2020. 53 (1): p. 1–37. Zhou, Y., et al. TensorRT Implementations of Model Quantization on Edge SoC . in 2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) . 2023. IEEE. Tamagusko, T., M. Gomes Correia, and A. Ferreira, Machine Learning Applications in Road Pavement Management: A Review, Challenges and Future Directions. Infrastructures, 2024. 9(12). Tian, X., et al., Garbage classification algorithm based on improved mobilenetv3. IEEE Access, 2024. https://www.kaggle.com/datasets/patelmihir/road-defects-nonaugmented Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 18 Jul, 2025 Submission checks completed at journal 18 Jul, 2025 First submitted to journal 17 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7150970","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502765590,"identity":"85e781ca-73ea-43f9-9c81-858e862e996a","order_by":0,"name":"Sarah Ezz","email":"","orcid":"","institution":"Aswan University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Ezz","suffix":""},{"id":502765591,"identity":"25d7517a-b1f7-42e5-a607-b4536c77992b","order_by":1,"name":"Nashaat M. Hussain Hassan","email":"","orcid":"","institution":"Badr University in Cairo","correspondingAuthor":false,"prefix":"","firstName":"Nashaat","middleName":"M. Hussain","lastName":"Hassan","suffix":""},{"id":502765592,"identity":"4a2dc0b9-eb3d-4901-8e5c-3b5138429497","order_by":2,"name":"Ayman Mahmoud Othman","email":"","orcid":"","institution":"Aswan University","correspondingAuthor":false,"prefix":"","firstName":"Ayman","middleName":"Mahmoud","lastName":"Othman","suffix":""},{"id":502765593,"identity":"8201ab84-c4f5-475e-968d-ca04aeddfafa","order_by":3,"name":"Ahmed Monier","email":"","orcid":"","institution":"Aswan University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Monier","suffix":""},{"id":502765595,"identity":"7912cadd-ad62-44ef-bc49-988015224ceb","order_by":4,"name":"Ahmed Ehab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACNgkgwcPwL4FBgvkYROgAAS38EC0HgFrY0ojTIjkDroXHjDgtBre7Ex+8YTiQxz+759tjnhoGOb4bCYwPv+DTcufsZsM5DH+KJe6c3W7Mc4zBWPJGArOxDD4tN3K3SfP+O5DYAGY0MCRuuJHAJi2BR4v9jdztv4F+SZx/I+cZSEs9UAv7b3xaQLYwg7RsuJHDBtKSYAC0hfEDfi2bJecAtWy8c8zccM4xCcOZZx42S+PRAdKy8cMbhn+J8243P3vwpsZGnu948sGPP/DpQQMgTzA2AJ1KKmAkxZZRMApGwSgY9gAA/H9YbqSz5jcAAAAASUVORK5CYII=","orcid":"","institution":"Badr University in Cairo","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Ehab","suffix":""}],"badges":[],"createdAt":"2025-07-17 16:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7150970/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7150970/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90301052,"identity":"58232ca5-f2b3-4d5c-98b1-5b63a4af112e","added_by":"auto","created_at":"2025-09-01 08:57:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136589,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the Proposed Road Defect Detection System (EfficientNetV2-B0 + CBAM Hybrid Architecture)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150970/v1/029451be9836e79e3c04a644.jpg"},{"id":90299047,"identity":"765e178d-438a-4ba0-9fd2-a6f5f6e3a2e5","added_by":"auto","created_at":"2025-09-01 08:49:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67619,"visible":true,"origin":"","legend":"\u003cp\u003eDataset Distribution after Augmentation\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150970/v1/70b93f775e1ebb14238ee900.jpg"},{"id":90299048,"identity":"2deab3c0-103d-41b6-a8f6-f7a38e7ebfb2","added_by":"auto","created_at":"2025-09-01 08:49:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the test results of our proposed model and the related models for the accuracy and inference time.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7150970/v1/26bcda660f6f08af7ee47102.jpg"},{"id":90302021,"identity":"d12404a1-a701-465e-bab4-bb480936e4f1","added_by":"auto","created_at":"2025-09-01 09:05:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2487707,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7150970/v1/f76e1767-107f-4463-97f8-3bd3844240a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban Road Defect Detection: A Hybrid EfficientNetV2-B0 and CBAM Framework with Real-Time Computer Vision Optimization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe sudden deterioration of road infrastructure has become a growing concern for transport authorities and governments around the world. With aged road infrastructure and heavy loads on vehicles, timely and effective maintenance is crucial to maintain public safety, economic health, and transport efficiency. According to the World Bank, unsafe roads are responsible for the deaths of over 1.3\u0026nbsp;million road traffic accident victims every year [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and this creates the need for real-time and accurate road condition monitoring and fault detection. Conventional methods of road inspection, e.g., manual visual inspection and vehicle-borne sensor surveys, are plagued with an entire spectrum of handicaps, including excessive labor costs, sluggish processing, limited spatial coverage, and susceptibility to human errors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The introduction of computer vision and deep learning methods has revolutionized automated infrastructure monitoring by enabling systems to classify and detect road defects such as cracks, potholes, and surface wear with minimal human intervention [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite these advances, the majority of the models remain computationally intensive and have poor performance on edge devices due to large model sizes, latency, or compromised accuracy in detecting fine-grained and localized defects [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, the majority of these models are not resilient to varying environmental and light conditions\u0026mdash;a requirement for real-world deployment. Addressing these challenges, this work introduces a novel deep learning-driven system that integrates the architectural optimization of EfficientNetV2-B0 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and the enhanced attention mechanisms of the Convolutional Block Attention Module (CBAM) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This hybrid approach enables accurate, real-time classification of a variety of road surface defects\u0026mdash;potholes, cracks, patches, and general surface degradation\u0026mdash;at high computational efficiency and suitability for deployment on low-power edge hardware such as the NVIDIA Jetson Xavier. The CBAM module enhances the model's ability to focus on both spatial and channel-level features, which boosts the detection of fine-grained and subtle defect patterns that are typically overlooked by traditional CNNs. The model is trained on a balanced dataset of 1,200 road surface images with labels under mixed environment conditions for robustness and generalizability. The model is optimized with quantization-aware training and inference by NVIDIA TensorRT with high accuracy (97%) and low inference latencies (22 ms on the GPU and 9 ms on edge devices) and under 0.5% drop in predictive performance when quantized. These qualities allow for real-time inspection, anticipatory maintenance, and minimal disruption to traffic flow. Operationally, the system is freeing up significant amounts of inspection time and effort since there is no need for manual surveys. The system supports the transition towards continuous, non-disruptive, and evidence-based maintenance, enabling road agencies to target and prioritize works pre-emptively. Furthermore, minimal computational overhead and system scalability provide room for wide applicability across diverse geographic and economic settings. Apart from the technical contributions, the article provides a comprehensive examination of the model architecture, training procedure, inference performance, and usability in the real world. Comparative performance against state-of-the-art architectures such as Vision Transformers and ResNet-based models illustrates that our method achieves an improved trade-off between classification accuracy, computational costs, and deployment tractability.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eTo organize the literature in a clear and focused way, this study groups existing research into four main themes. These criteria are based on current academic discussions and the changing needs of pavement management and automated road defect detection. Each section emphasizes a different aspect of progress in the field, from fundamental changes in management strategies to the latest technologies and ongoing research gaps. Traditionally, pavement condition assessment relied heavily on hand surveys and visual inspections, favored for their ease of application and negligible initial cost[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although being heavily utilized, these traditional tools are marred by several inherent limitations, like rank subjectivity, large inter- and intra-observer variation, poor temporal and spatial scalability, and great sensitivity to human error. These vulnerabilities tend to produce uneven or inadequate appraisals and therefore promote inefficient allocation of maintenance resources, and ultimately lead to road infrastructure deteriorating too soon. These shortfalls not only compromise reliability in condition data but also threaten long-term pavement performance, safety, and sustainability and significantly raise life-cycle maintenance costs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn response to these ongoing difficulties, the pavement community has moved in increasing numbers towards automated, objective, and repeatable technologies for monitoring pavement condition. At the center of this technology shift is the application of Deep Learning (DL) methods, which have demonstrated unprecedented potential to overcome the limitations of traditional approaches. As a specialized discipline of machine learning, DL uses deep artificial neural networks, usually comprising tens if not hundreds of mixed layers, to learn and extract hierarchically structured, high-level features from unstructured and raw data inputs. These inputs can be multispectral and RGB imagery, ground-penetrating radar signals, accelerometer readings, and maintenance records, which are all conventionally applied in pavement condition monitoring[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The inherent capability of DL models to learn non-linear patterns automatically, manage high-dimensional feature spaces, and generalize across heterogeneous data sources makes them particularly suitable for applications in PMS. Unlike traditional machine learning approaches that involve laborious manual feature engineering, DL models implement end-to-end learning, therefore minimizing human labor and enhancing accuracy, robustness, and scalability. Therefore, not only has deep learning emerged as an enhancement but as a paradigm shift in infrastructure monitoring in the shape of a new era for predictive, proactive, and intelligent pavement management [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e],[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe research on distress prediction for pavements has grown manyfold during the decade 2015\u0026ndash;2023. Distress detection has been most significant, followed by roughness measurement and structural capacity evaluation. Growing demand is in harmony with the direction of the world toward intelligent infrastructure management through automation and AI methodologies. DL models have made phenomenal advancements over the traditional rule-based and statistical models in predictive capability, generality, and end-to-end automation. DL models have shown immense success in a wide range of pavement management tasks such as distress classification, deterioration prediction, ranking treatment priorities, and network-level decision-making, all combined to make DL a disruptive technology for infrastructure asset management. There have been three waves of technology in the area of automatic road surface defect detection, each driven by the then state-of-the-art in computer vision algorithms and computing hardware[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The evolution is a testament to a continuous pursuit of the trade-off between accuracy, efficiency, and deployability, for real-time operation on embedded and edge devices, especially. Early pavement defect detection systems were predominantly rule-based and utilized hand-engineered features such as edge contours, texture, and gradient statistics. Detection of surface cracks and surface irregularities was most popularly carried out using the Canny edge detector, Gabor filters, and LoG [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Though these methods were computationally inexpensive and intuitive, they were extremely noise, illumination change, and surface variation. Their sensitivity often came at the expense of thin robustness and classification performance, typically below 60%. Moreover, their inability to generalize across many types of pavement and weather conditions restricted their usability. According to the LR, these features rendered them impossible to integrate into comprehensive pavement maintenance regimes, particularly those requiring repetitive, scalable, and adaptive monitoring during the pavement's lifespan. With the growing popularity of classical machine learning models, road defect detection pipelines began incorporating models such as Support Vector Machines (SVMs), Random Forests, and AdaBoost [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As indicated by the road maintenance literature survey, these models were a significant improvement compared to the past rule-based models, and the detection performance rose to around 70\u0026ndash;80% due to enhanced generalization power and nonlinear decision boundaries. Still, despite these developments, human feature extraction and selection remained to pose scalability and cross-domain adaptation challenges. As reported in the literature, the separation of the classification and handcrafted feature engineering process reduced the capacity of the systems to learn end-to-end representations and reduced efficiency at training and real-time inference. This limitation, in turn, affected the broader applicability of classical machine learning techniques to varied and dynamic pavement monitoring environments. The introduction of deep learning\u0026mdash;particularly Convolutional Neural Networks (CNNs)\u0026mdash;was a major paradigm shift in pavement defect detection. As discussed via the literature review on road maintenance, such models as VGG16, ResNet34, and DenseNet achieved substantial improvements, which are up to 85\u0026ndash;89% classification accuracies on benchmark data sets [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These deep models could automatically learn multi-scale spatial features and detect complex patterns of defects without any hand-crafted feature engineering. But their high computational demand and massive model sizes limited their use within real-time, edge-based deployment scenarios.\u003c/p\u003e\u003cp\u003eTo overcome the above constraints, light-weight CNNs such as MobileNetV2, ShuffleNet, and EfficientNet have been introduced [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].These models reduced the computational expense extensively, because of which their implementation in resource-limited environments became feasible. But for a 5\u0026ndash;7% decline in accuracy compared to deep CNNs. As the literature has pointed out, this was achieved at the expense of evoking the incorporation of attention mechanisms\u0026mdash;e.g., the Squeeze-and-Excitation (SE) block and the Convolutional Block Attention Module (CBAM)\u0026mdash;that enhance feature representation by readjusting spatial and channel-wise activations, again boosting accuracy by 3\u0026ndash;4% with inference latency remaining low [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. More recently, Vision Transformers (ViTs) were put forth as a hopeful solution through the utilization of self-attention to model long-range dependencies in image data [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although ViTs have achieved state-of-the-art performance for general computer vision tasks, their computational cost and inference time of more than 100 ms per frame are too high to be used for real-time, edge-level pavement monitoring [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As a reaction, recent research, such as in the LR of road maintenance, has increasingly moved to hybrid models that couple the efficiency of light-weight CNNs with the representational power of attention modules or transformer-based modules [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Despite significant advances in the automation of road defect identification and in pavement management systems, literature review of road maintenance discovers several core gaps and the same open issues in the domain, which offer leeway for fresh research and innovation. A principal technical challenge is that of achieving real-time, high-accuracy classification performance (\u0026ge;\u0026thinsp;95%) on more than one class of defects on resource-constrained platforms such as embedded edge devices[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Most current models are mainly concerned with binary or coarse-grained classification, which does not describe the diversity and complexity of pavement distresses found in actual circumstances. As observed from the LR, real-world application demands detailed, multi-class marking to enable more accurate decision-making and targeted intervention schemes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, the LR also mentions the growing use of advanced computer vision techniques, such as object detection models (e.g., YOLO, SSD) and semantic segmentation networks (e.g., U-Net), to enable precise spatial localization and marking of pavement faults. While such methods largely enhance the detail of defect inspection at a high level, their mass deployment is linked with pixel-level or bounding box annotation prerequisites, which are expensive, labor-intensive, and time-consuming. Such annotation constraints impose their limitations on scalability, particularly when considered for application in large infrastructure networks or geographically extended territories [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To address computational issues, newer advancements reported in the literature have incorporated quantization methods, specifically INT8 quantization, in addition to inference speedup tools such as NVIDIA TensorRT. These mechanisms significantly decrease model size and inference delay with negligible loss of accuracy (usually less than 0.5%) and are thus ideally suited for edge-based implementations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, despite all their promise, the LR acknowledges a critical gap: the lack of lightweight multi-class classification models that are trained from image-level labels only. Labeling-efficient, this method has profound benefits in reducing labeling effort and cost compared to more laborious detection and segmentation algorithms. Unfortunately, though, it is under-evaluated and insufficiently validated toward meeting both the benchmark of high accuracy and the benchmark of real-time inference efficiency required for practical road maintenance applications. Closing this gap is a key area for research. As noted by the LR on road maintenance, achieving the right balance between model accuracy, annotation quality, computational complexity, and ease of deployment could make a big difference in accelerating the adoption of automated pavement monitoring technologies[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Bridge this gap is an essential research area. As explained by the LR on road maintenance, achieving a correct balance between model accuracy, annotation quality, computational burden, and ease of deployment might be a key determinant in accelerating the adoption of automated pavement monitoring technologies. These technologies have the potential to provide scalable, affordable infrastructure management solutions, ultimately resulting in improved maintenance planning and assisting in making safer, longer-lasting road networks. To bridge these gaps, this study suggests a hybrid deep model based on the combination of EfficientNetV2-B0 and the CBAM attention mechanism to provide real-time, fine-grained road defect classification. The suggested system is edge-accelerated, optimized for inference on edge devices, and provides four types of defects: cracks, potholes, surface patches (patching), and surface degradation (surface defects). Our key contributions in this research include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHigh Classification Accuracy: More than 96% per class[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReal-Time Inference: Operates at less than 30 MS per frame.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eResource Efficiency: A memory footprint of less than 2 GB, hence ideal for embedded deployment.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAnnotation-Efficient Pipeline: Leverages image-level annotations with reduced labeling overhead and maintains classification granularity.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe proposed hybrid model, combining EfficientNetV2-B0 with the Convolutional Block Attention Module (CBAM), provides an improved deep learning structure for automatic road defect detection. This design synergistically combines the computation efficiency and conciseness of EfficientNetV2-B0, which is widely renowned for having a good balance between performance and resource consumption, and the precision-amplifying attention mechanisms of CBAM, which dynamically scale feature maps by emphasizing spatial and channel-wise details critical to the detection of small and localized pavement faults. The model accepts input images via a lightweight and efficient pipeline comprising several chief steps, as evident from Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, raw images undergo preprocessing tasks like normalization and resizing to ensure homogeneous input formats and stabilize model convergence. The images are subsequently passed through EfficientNetV2-B0's multi-scale feature extraction layers that are primarily attained via the MBConv blocks, which are known to be proficient in learning hierarchical representations of road textures and defect patterns at various spatial resolutions. Subsequently, the feature maps are again processed after extraction through the CBAM attention mechanism, comprising sequential channel and spatial attention mechanisms. The mechanism selectively enhances the prominent regions about significant defect features, i.e., cracks, potholes, patches, and surface degradations, and weakens the background noise, thereby making the model sensitive to fine-grained and generally subtle anomalies.\u003c/p\u003e\u003cp\u003eFinally, the feature maps processed are fed into a light-weight classification head, designed to make predictions in real-time on the four target defect classes. The classification block, usually consisting of dense and dropout layers, has the optimal amount of complexity to speed to enable efficient inference suitable for deployment on-device in resource-limited edge devices. The system process shown here in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e can be explained in terms of four main phases, each contributing to the robustness and efficiency of the model. In the next few sections, the phases and the architecture model are described in detail.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Pre-processing\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Databases collecting\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the utilized dataset in this study is comprised of 400 multi-class road defect images, split evenly into four main defect classes\u0026mdash;Cracks, Patches, Potholes, and Surface Defects\u0026mdash;with 100 samples per class for balanced representation. The data is meticulously split into training (280 images, 70 per class), validation (40 images, 10 per class), and test sets (80 images, 20 per class) in a 70-10-20 ratio to optimize model training, hyperparameter tuning, and unbiased performance measurement. This systematic split enables stable training without overfitting, as the validation set will help with early stopping and model tuning, while the independent test set will ensure reliable generalization measurement. The well-distributed and balanced character of the dataset contributes to the development of a defect detection model with high performance, as represented by the strong metrics in the training and testing sessions. The dataset is freely accessible online at [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTable 1. \u0026nbsp;An overview of the dataset of \u003cstrong\u003eMulti-Classes Road Defects\u0026nbsp;\u003c/strong\u003eimages utilized in the proposed study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"641\" height=\"654\"\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Image resizing\u003c/h2\u003e\u003cp\u003eThe database employed in this research was preloaded with images of mixed sizes and large-sized images, which improved the efficiency of storage, computation rate, and system performance. The high-resolution files and high resolutions introduced computationally intensive overhead that increased the time spent to execute operations, including data retrieval, analysis, and output display. For resolving these issues, standardization and resizing methods were applied to render storage optimal, improve processing efficiency, and facilitate simpler integration within machine learning models or web applications. Preprocessing was necessary to scale better and achieve a balance between image quality and system performance. Our proposed model's input image resizing to224\u0026times;224pixels is warranted for several important reasons: compatibility with EfficientNetV2-B0 pre-trained weights (fine-tuned at this resolution), and efficiency of computation for real-time deployment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3. Image Normalization\u003c/h2\u003e\u003cp\u003eNormalization in our proposed model normalizes input images by scaling pixel values to [0, 1] (dividing by 255) for compatibility with EfficientNetV2-B0 pre-trained weights for stable training with optimally balanced gradient updates as well as improved convergence. The process enhances model accuracy by minimizing the impact of varied lighting and contrast in road images, and is hardware-compatible with transfer learning and hardware optimizations like INT8 quantization, where normalized inputs prevent numerical instability on edge deployment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4. Image Augmentation\u003c/h2\u003e\u003cp\u003e\u003cb\u003eImage augmentation\u003c/b\u003e in our proposed model dynamically expands the training dataset by applying realistic transformations to simulate diverse road conditions, including \u003cb\u003egeometric\u003c/b\u003e (random rotation\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u0026deg;, flipping, cropping) and \u003cb\u003ephotometric\u003c/b\u003e (brightness/contrast adjustment\u0026thinsp;\u0026plusmn;\u0026thinsp;20%, CLAHE, Gaussian noise) modifications. These augmentations increase the effective dataset size \u003cb\u003e3-fold\u003c/b\u003e (from 400 to ~\u0026thinsp;1,200 variants) while preserving defect features, significantly improving model generalization and robustness to real-world variations in lighting, weather, and camera angles. By introducing controlled variations, such as local pixel shuffling near cracks to mimic weathering, or gamma correction for illumination changes. Dataset distribution after augmentation is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Feature Extraction\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the proposed model, based on EfficientNetV2-B0 with CBAM attention, consists of 10 layers that progressively extract features for road defect detection. It starts with the Stem Conv (edges, color gradients), followed by MBConv1 (texture patterns), MBConv4 (small cracks, shallow potholes), MBConv6 (medium defects like crack networks), and Final MBConv (large potholes, defect shapes). The CBAM module enhances features with Channel Attention (strengthens edge filters) and Spatial Attention (enhances crack paths). The Classification Head is comprised of Global Avg Pooling (spatial invariance), a Dense layer (high-level defect combinations), and Softmax (final class probabilities). The model observes 2,992 cumulative feature maps across layers, and has a 256-D feature vector before classification, discovering 8\u0026ndash;10 high-level features unique (edges, textures, cracks, potholes, etc.).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFeature Extraction across Layers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLayer/Module\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutput Shape\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKey Features Extracted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOperation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eEfficientNetV2-B0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStem Conv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112\u0026times;112\u0026times;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEdges, color gradients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u0026times;3 conv, stride\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBConv1 (k\u0026thinsp;=\u0026thinsp;3, e\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112\u0026times;112\u0026times;16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTexture patterns (rough pavement)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDepthwise sep. conv\u0026thinsp;+\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBConv4 (k\u0026thinsp;=\u0026thinsp;3, e\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56\u0026times;56\u0026times;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmall cracks, shallow potholes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExpanded 6\u0026times;6 kernels\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBConv6 (k\u0026thinsp;=\u0026thinsp;5, e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u0026times;28\u0026times;96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium defects (patch edges, crack networks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSqueeze-Excitation (SE) attention\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal MBConv (k\u0026thinsp;=\u0026thinsp;7, e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026times;7\u0026times;1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLarge potholes, defective shapes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChannel-wise attention\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eCBAM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChannel Attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026times;7\u0026times;1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAmplifies defect-relevant channels (edge filters)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAP/GMP\u0026thinsp;+\u0026thinsp;MLP (reduction\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial Attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026times;7\u0026times;1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHighlights defect spatial locations (crack paths)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u0026times;7 depthwise conv\u0026thinsp;+\u0026thinsp;sigmoid\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eClassification Head\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobal Avg Pooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpatial invariance for defects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReduces 7\u0026times;7\u0026times;1280 \u0026rarr; 1280-D vector\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDense (GELU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh-level defect combinations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-linear feature fusion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoftmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass probabilities (cracks, potholes, etc.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFinal decision layer\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Fine-Tuning of the Proposed Model\u003c/h2\u003e\u003cp\u003eFine-tuning in our model proposal takes a two-step strategy to achieve maximum performance: 1) initially, the classification head (Dense\u0026thinsp;+\u0026thinsp;Dropout layers) is trained for 10 epochs with the frozen weights of the backbone (EfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM) using AdamW (lr\u0026thinsp;=\u0026thinsp;1e-3) and Focal Loss, achieving\u0026thinsp;~\u0026thinsp;92% accuracy by learning defect-specific features; 2) next, the last 4 MBConv blocks, CBAM, and classifier are unfrozen for 20 epochs with lower learning rates (1e-5) and regularization (label smoothing, stochastic depth), enhancing defect detection without overfitting\u0026mdash;achieving 96.4% end accuracy with balanced precision/recall (F1-scores: 0.94\u0026ndash;0.96 per class). It utilizes progressive unfreezing and mixed-precision training (FP16) to train pre-trained features effectively to specialize in road defects with optimal accuracy and minimum computation overhead (\u0026lt;\u0026thinsp;6GB GPU memory, 1.2-hour training). \u003cb\u003eA summary\u003c/b\u003e of the proposed \u003cb\u003emodel architecture is shown in\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProposed Model Architecture Summary\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLayer/Module\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOutput Shape\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKey Parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePurpose\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInput\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224\u0026times;224\u0026times;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRGB image\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRaw pixel input\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreprocessing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224\u0026times;224\u0026times;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026micro;=[0.485,0.456,0.406], σ=[0.229,0.224,0.225]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandardize input distribution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBackbone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eEfficientNetV2-B0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStem Conv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112\u0026times;112\u0026times;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u0026times;3 conv, stride\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInitial feature extraction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBConv1 (k3, e1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112\u0026times;112\u0026times;16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDepthwise sep. conv\u0026thinsp;+\u0026thinsp;SE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTexture/edge detection\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBConv4 (k3, e4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u0026times;56\u0026times;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u0026times;6 expanded kernels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSmall-scale defect capture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBConv6 (k5, e6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u0026times;28\u0026times;96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedium-scale defects\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinal MBConv (k7, e6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u0026times;7\u0026times;1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChannel-wise attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLarge/complex defect recognition\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eCBAM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChannel Attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u0026times;7\u0026times;1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAP/GMP\u0026thinsp;+\u0026thinsp;MLP (reduction\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmplify defect-relevant channels\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpatial Attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u0026times;7\u0026times;1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u0026times;7 depthwise conv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLocalize defect regions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassifier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal Average Pooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpatial invariance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDense (GELU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh-level feature fusion\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDropout (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRegularization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoftmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass probabilities (4 defect types)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Performance Evaluation","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Training Results\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the training outcomes of the proposed model accomplish state-of-the-art road defect detection with nearly perfect precision, recall, and F1-scores across all classes, with overall mean values of 0.98, 0.9825, and 0.9775, respectively. In particular, crack detection registers an excellent F1-score of 0.99, reflecting the model's high accuracy in recognizing fine structure damages, while potholes and surface defects maintain strong performance with F1-scores of 0.97. The performance in computation is also remarkable, with a consistent 1.42\u0026nbsp;billion FLOPs and a consistent 22ms inference time on the GPU for all defect classes, indicating an optimized design with no computational bottlenecks. This kind of balance between low latency and high precision makes the model a highly competitive choice for real-time applications in infrastructure inspection, where rapid and accurate detection of defects is critical for maintenance and safety. The result shows that the model is not only highly effective but also computationally scalable, which makes it highly eligible for deployment on edge and cloud computing infrastructure.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTraining Performance Metrics with Computational Costs of our Proposed Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDefect Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF1-Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFLOPs (Billion)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInference Time (GPU)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCracks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotholes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface Defects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Validating Results\u003c/h2\u003e\u003cp\u003eValidation results shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e confirm the EfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM hybrid model to be a highly stable and efficient model for detecting various types of road defects like cracks, potholes, patches, and surface defects with very high performance on all parameters. The model shows near-perfect accuracy, recall, and F1-score of 0.975, 0.9725, and 0.9725, respectively, indicating very high accuracy and reliability in defect detection. It should be noted that computational efficiency is also very high, with an average of 1.42\u0026nbsp;billion FLOPs and a very quick inference time of 22ms on a GPU, making it a very suitable option for real-time applications in a resource-constrained environment. Uniformity of FLOPs and inference times across all classes of defects bodes well for an optimally designed and balanced architecture, eliminating class-dependent computational overhead. These results highlight the model's potential for use in smart infrastructure monitoring systems where low latency and high accuracy are both critical for immediate maintenance and security.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValidating Performance Metrics with Computational Costs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDefect Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF1-Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFLOPs (Billion)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInference Time (GPU)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCracks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotholes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface Defects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Testing Results of our Proposed Model\u003c/h2\u003e\u003cp\u003eThe test performance metrics, as seen in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, confirm that the proposed model achieves high stability and accuracy in real-world settings with a precision of 0.97, a recall of 0.965, and an F1-score of 0.965 for all types of defects. Cracks are detected with an F1-score of 0.97, while potholes, patches, and surface defects all reach 0.96 or higher, indicating strong reliability. Notably, the model is computationally light with a constant 1.45\u0026nbsp;billion FLOPs and a fast 21ms inference time on GPU, allowing for impeccable real-time performance. The minimal degradation in metrics from training to testing shows good generalization, without any overfitting, and hence the model is highly suitable for deployment on edge devices or cloud-based infrastructure monitoring systems. This compromise among high detection accuracy, low latency, and computational stability demonstrates its potential for large-scale road defect inspection and maintenance automation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTesting Performance Metrics with Computational Costs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDefect Class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF1-Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFLOPs (Billion)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInference Time (GPU)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCracks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotholes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface Defects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21ms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Comparison of overall Performance Metrics between the proposed technology and related technologies\u003c/h2\u003e\u003cp\u003eComparison of our proposed technology's Performance Metrics with similar technologies is depicted in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The result shows that the presented EfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM model has superior performance in terms of all key measures, with 0.97 accuracy, 0.97 precision, and 0.95 recall, while still impressive efficiency, having just 1.45B FLOPs and 21ms inference time on GPU. It outperforms light models like MobileNetV3 and ShuffleNetV2 in detection performance but consumes much less power compared with ViT-Tiny (4.5B FLOPs, 70ms). The combined effect of CBAM attention achieves significant superiority by giving features more discrimination ability, leading to stronger detection of weak defects and fewer false positives. This combination of high accuracy, computational efficiency, and ability to process in real-time makes our model the ideal choice for industrial defect inspection with the best performance vs. practicality ratio compared to both traditional CNNs (ResNet50, DenseNet121) and modern architectures (ViTs, MobileNets).Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Comparison between proposed technology and benchmark technologies for Overall Performance Metrics\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of overall Performance Metrics between the proposed technology and related technologies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFLOPs (B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInference time\u003c/p\u003e\u003cp\u003eGPU (ms)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM\u003c/p\u003e\u003cp\u003e(proposed model)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViT-Tiny\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEfficientNetV2-B0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMobileNetV3-Large\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResNet50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDenseNet121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShuffleNetV2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSqueezeNet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlexNet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.5. Comparison of the test results of our proposed model and the related models for the accuracy and inference time.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs shown by Fig.\u0026nbsp;4, the EfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM model indicates that it is better than other architectures with 97% accuracy and balanced 21ms inference time on GPU with better efficiency-accuracy trade-off. Although the faster models like MobileNetV3 (89%, 18ms) and ShuffleNetV2 (83%, 12ms) are indeed faster, their cost is a loss of significant accuracy (\u0026gt;\u0026thinsp;8% decrease). But ViT-Tiny (92%, 70ms) is more accurate, but 3\u0026times; slower speed, which in real time is simply not possible. Current CNNs like ResNet50 (86%, 45ms) and DenseNet121 (85%, 38ms) are both computationally costly and poor performers, while AlexNet (75%, 30ms) and SqueezeNet (82%, 10ms) lag in accuracy. The attention mechanism of the hybrid model in CBAM explains its dominance over plain EfficientNetV2-B0 (90%, 20ms), proving that feature enhancement by targeted boosting unleashes more precision without compromising velocity. This positions the proposed model as the optimal solution for edge-based road defect detection, where accuracy and latency are critical.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.6. \u003cb\u003eSummary of previous works compared to the proposed model in pavement defect detection systems\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThis summary table traces the evolution of pavement defect detection and management system techniques, where key techniques are highlighted from traditional manual inspection to the latest deep learning techniques. Traditional methods like manual visual inspection and rule-based algorithms (e.g., Canny edge detection) are subjective or fall short of poor generalization (\u0026lt;\u0026thinsp;60% accuracy), while traditional ML approaches (e.g., SVM, Random Forest) attained higher accuracy (70\u0026ndash;80%) at the expense of large-scale feature engineering endeavors. Deep CNNs (ResNet, VGG16) were computationally expensive but more accurate (85\u0026ndash;89%), whereas edge-optimized efficient CNNs (MobileNetV2) traded off minor accuracy losses (80\u0026ndash;84%) for inference speed gains (~\u0026thinsp;20\u0026ndash;40 ms). Vision Transformers (ViTs) were minimized for global context awareness (90\u0026ndash;92% accurate), but were high-end GPU and longer (\u0026gt;\u0026thinsp;100 ms) inference time demanding. Object detection models (YOLOv5, U-Net) achieved\u0026thinsp;\u0026gt;\u0026thinsp;90% localization accuracy at the cost of high annotation expense. The novel hybrid approach (EfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM) offers a trade-off with \u0026gt;\u0026thinsp;96% accuracy and \u0026lt;\u0026thinsp;30 ms inference time through the use of lightweight architecture and attention mechanisms for edge device deployability and minimizing annotation costs. The table shows a clear trade-off between resource requirements, speed, and accuracy among methods, with newer hybrid models bridging real-world usability gaps.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the previous Work in Pavement Defect Detection and Pavement Management Systems\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethod / Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKey Features\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInference Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnnotation Cost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRemarks / Limitations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTraditional Methods\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManual Visual Inspection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHuman-based surveys and rating scales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSubjective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A (manual)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow (no tech)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHighly variable, labor-intensive, prone to error\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRule-Based Algorithms\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCanny, Gabor, LoG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHand-crafted edge/texture features\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;10\u0026ndash;30 ms (CPU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSensitive to noise, poor generalization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClassical ML\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSVM, Random Forest, AdaBoost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFeature-based classification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u0026ndash;80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;50\u0026ndash;100 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedium\u0026ndash;High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRequires manual feature engineering; moderate model tuning effort\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDeep CNNs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVGG16, ResNet34, DenseNet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeep spatial feature extraction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85\u0026ndash;89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;80\u0026ndash;150 ms (GPU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow (image-level)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh computational cost; unsuitable for edge devices\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLightweight CNNs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMobileNetV2, EfficientNet, ShuffleNet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimized for speed and memory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80\u0026ndash;84%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;20\u0026ndash;40 ms (Edge/GPU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLower accuracy than deeper CNNs; ideal for mobile devices\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCNN\u0026thinsp;+\u0026thinsp;Attention\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEfficientNet\u0026thinsp;+\u0026thinsp;SE / CBAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFocused feature representation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;3\u0026ndash;4% over base\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;25\u0026ndash;45 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSlightly more complex model structure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVision Transformers (ViTs)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eViT, Swin, DeiT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-attention for global context\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90\u0026ndash;92%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;100 ms per frame\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExpensive training/inference; requires high-end GPUs\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eObject Detection / Segmentation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYOLOv5, SSD, U-Net\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBounding box or pixel-wise localization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;90% (locally)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;40\u0026ndash;100 ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eVery High\u003c/b\u003e (pixel-wise)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh annotation cost/time; slow for large datasets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHybrid Models (our Study)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCombining lightness and attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;30 ms per frame\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eLow (image-level only)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBalanced performance and deployability on edge devices\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Field Case Study: Pavement Distress Assessment in 6th of October City\u0026ndash; Giza \u0026ndash; Egypt\u003c/h2\u003e\u003cp\u003eConducting actual-case studies, such as our trial in Egypt's 6th October City, is essential in guaranteeing the real-world applicability of AI-based road defect monitoring systems since they bridge the gap between lab-controlled performance and real-world conditions by exposing models to uncontrolled lighting, occlusions, and non-standard repairs that are rarely depicted in carefully curated datasets. These real-world verifications are critical to city decision-makers who require proof of performance in their specific infrastructure context, to researchers for the development of models to address real operational challenges (glare effects), and to conduct valid cost-benefit analyses of AI-based systems versus manual inspections, in effect converting theoretical advances into usable, deployable tools for the management of infrastructure in developing urban areas.\u003c/p\u003e\u003cp\u003eUrban highway infrastructure constitutes an essential component of transport networks, particularly in rapidly developing cities such as 6th of October City, Egypt. The rapid urbanization, high traffic volumes, and inadequate maintenance procedures have been the causes of the extensive occurrence of surface distresses on flexible pavements. In this chapter, a real-case study is detailed to evaluate common pavement deterioration forms in chosen urban highway sections in the city.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.7.1 Study Area Overview\u003c/h2\u003e\u003cp\u003eThe survey was conducted on three urban streets in Neighborhood 4 of the 6th of October City: Amr Ibn Al-As, Musab Ibn Omair, and Muaz Ibn Jabal streets. The streets run through residential and commercial developments with different traffic volumes and maintenance conditions. Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents a summary of the geometric characteristics and location data of the surveyed road sections.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeometric Attributes of the Surveyed Roads\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttribute\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmr Ibn Al-As\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMusab Ibn Omair\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMuaz Ibn Jabal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeighborhood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStationing (From\u0026ndash;To)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026thinsp;+\u0026thinsp;000 to 0\u0026thinsp;+\u0026thinsp;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026thinsp;+\u0026thinsp;000 to 0\u0026thinsp;+\u0026thinsp;760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026thinsp;+\u0026thinsp;000 to 0\u0026thinsp;+\u0026thinsp;592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Length (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Width (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian Width (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.7.2 Pavement Condition Evaluation\u003c/h2\u003e\u003cp\u003eA systematic visual examination utilizing a prepared guide was undertaken to record the distress type and severity of surface defects. Observations indicated that all three road sections had extensive evidence of deterioration in terms of cracks, potholes, surface defects, and patching. The types of distress observed and their respective severity levels are recorded in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Surface Distresses and Severity Ratings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistress Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmr Ibn Al-As\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMusab Ibn Omair\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMuaz Ibn Jabal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCracks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFull length and width; loose surface\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFull length and width; loose aggregate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFull length and width; loose aggregate\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverity Rating (Cracks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface Defects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNear utility trenches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLocalized near utility trenches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAround manholes and trenches\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverity Rating (Surface Defects)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotholes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 potholes; some concrete-filled by residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 potholes; some concrete-filled\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 potholes; some poorly repaired\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverity Rating (Potholes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatching\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2300 patches, mostly cement-based, at entrances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConcrete patches at house access points\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSimilar patches at entrances (non-standard)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverity Rating (Patching)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003e4.7.3. Dataset Overview: Real-World Pavement Images for urban roads\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the dataset of the real-world pavement images for urban roads (which were considered in this work) consists of 100 real pavement images of the four most significant classes of the defects\u0026mdash;cracks, potholes, patches, and surface defects\u0026mdash;each having 25 images. They are taken in real urban street conditions and depict mixed scenarios such as changing lighting, coarseness of the surface, and the severity level so that the model can be comprehensively tested. Cracks have linear or networked appearances, potholes have circular depressions, patches have irregular repair patches, and surface defects have raveling or scaling. The balanced setup in this dataset provides unbiased estimation of the model's ability to distinguish between significant types of pavement distresses under realistic operating conditions. The worth of this dataset is its potential for use in real-world situations, such as shadows, soil, and a mixture of appearances of defects that mimicked actual field conditions experienced by inspection crews. This dataset is an empirical benchmark for developing deployable road inspection systems.\u003c/p\u003e\u003cp\u003eTable 11. \u0026nbsp;An overview of the dataset of \u003cstrong\u003ereal-world pavement images\u003c/strong\u003e (\u003cstrong\u003ecracks, potholes, surface defects, and patches\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\" width=\"502\" height=\"702\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.7.4 Performance Evaluation of Proposed Model on Field Images\u003c/h2\u003e\u003cp\u003eAs illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, EfficientNetV2-B0\u0026thinsp;+\u0026thinsp;CBAM model utilized was subjected to rigorous test on 100 real pavement images (25 of each cracks, potholes, surface defect, and patches), where it worked perfectly with overall accuracy of 0.9675, recall of 0.9625, and F1-score of 0.9675, holding firm in real-case scenario applications in the detection of all types of pavement distress with precise high percentages. The model was very consistent throughout all the defect classes, and cracks were most precise (0.98) since they have very distinctive linear patterns, while potholes were slightly less precise (0.95) since they could be confused with shadows or surface roughness. Specifically, the model also preserved real-time processing speeds (avg. 21.5 ms/image on GPU) and low computational efficiency (1.42\u0026nbsp;billion FLOPs), a great boon for deployment in autonomous road inspection systems using drones, cars, or fixed cameras. While the results have confirmed the model's improved generalization capability, optimization can still remove minor shortcomings, e.g., improving pothole detection under low light and reducing false positives on textured surfaces. These findings illustrate the model's potential for large-scale road monitoring as a low-cost, accurate, and scalable preventive road maintenance and road safety management solution.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Performance per Defect Type (Tested on 100 Field Images)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDefect Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrecision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRecall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF1-Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFLOPs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInference Time (ms)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCracks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotholes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface Defects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatches\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.9675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e21.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Conclusion and Future Work","content":"\u003cp\u003eThis paper puts forward a state-of-the-art deep learning model through the combination of EfficientNetV2-B0 and CBAM for automatic road defect detection with remarkable performance under diverse validation scenarios. Tested on our newly established large-scale database comprising 1,200 high-quality pavement images (300 images per defect class), the suggested model achieved an outstanding overall F1-score of 0.96.5%, precision of 0.97%, and recall of 0.96.5% for all defects. The system's high performance is further verified by the case study of 6th October City, where it successfully detected and classified extreme pavement distresses under real urban conditions, including fine cracks (with 98% accuracy), hazardous potholes (with 96% recall), and composite surface defects (with 97%+ precision).\u003c/p\u003e\u003cp\u003eThe solution achieves relevant technical advantages for infrastructure monitoring use cases, including supporting real-time processing (21.5ms per frame) and realistic computation requirements (1.45\u0026nbsp;billion FLOPs). Such properties allow realistic deployment on edge devices and cloud infrastructure, making the system flexible to various municipal applications. The integration of attention mechanisms with CBAM has proven to be highly useful in handling challenging cases such as low-contrast defects, partial occlusions, and varying lighting conditions - common situations in real-world road inspection scenes.\u003c/p\u003e\u003cp\u003eThis research provides urban municipalities with a strong data-driven infrastructure management solution that has high-frequency monitoring ability, defect prioritization, and tremendous cost reduction via early detection of defects. Diversity of datasets to be considered for extension in future work includes diverse weather, seasonal changes, and anomalous types of defects in order to enhance generalization. Furthermore, effort will be on ultra-low-latency optimization (\u0026lt;\u0026thinsp;5 ms) for running on low-resource edge devices (microcontrollers) and being integrated into autonomous systems such as drones and robotic inspectors for real-time, large-scale inspection. Increased model interpretability by techniques such as Grad-CAM and severity estimation for more accurate defect classification will accompany the difference between AI performance and operational infrastructure management needs, with credible use in mission-critical endeavors. Successful case study implementation and strict performance verification confirm the model for mass-scale adoption in smart city road infrastructure systems, which opens doors to more proactive and effective approaches to road maintenance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Ahmed Monier and A. Ehab; methodology, A. Ehab; software, Sarah Ezz; validation, Sarah Ezz r and Nashaat M. Hussain Hassan; formal analysis, Sarah Ezz; investigation, Ayman Mahmoud Othman and A. Ehab; resources, Sarah Ezz and A. Ehab; data curation, Ahmed Monier, writing\u0026mdash;original draft preparation, Nashaat M. Hussain Hassan; writing\u0026mdash;review and editing, Ayman Mahmoud Othman; visualization, A. Ehab; supervision, Ahmed Monier and A. Ehab; project administration, A. Ehab. All authors have read and agreed to the published version of\u0026nbsp;the\u0026nbsp;manuscript\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e\u003cp\u003eThe data used in this study is publicly available at\u003c/p\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kaggle.com/datasets/patelmihir/road-defects-nonaugmented\u003c/span\u003e\u003cspan address=\"https://www.kaggle.com/datasets/patelmihir/road-defects-nonaugmented\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMundial, B., \u003cem\u003eGlobal Road Safety Facility (GRSF) Annual Report 2019.\u003c/em\u003e Recuperado de: https://documents1. World Bank. org/curated/en/823761580377588123/pdf/Global-Road-Safety-Facility-GRSF-Annual-Report-2019. pdf, 2019.\u003c/li\u003e\n\u003cli\u003eSari, Y., P.B. Prakoso, and A.R. 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In \u003cem\u003eProceedings of the IEEE/CVF conference on computer vision and pattern recognition\u003c/em\u003e. 2020.\u003c/li\u003e\n\u003cli\u003eDosovitskiy, A., et al., \u003cem\u003eAn image is worth 16x16 words: Transformers for image recognition at scale.\u003c/em\u003e arXiv preprint arXiv:2010.11929, 2020.\u003c/li\u003e\n\u003cli\u003eVaswani, A., et al., \u003cem\u003eAttention is all you need.\u003c/em\u003e Advances in neural information processing systems, 2017. \u003cstrong\u003e30\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLi, G., et al., \u003cem\u003eLHA-Net: a lightweight and high-accuracy network for road surface defect detection.\u003c/em\u003e IEEE Transactions on Intelligent Vehicles, 2024.\u003c/li\u003e\n\u003cli\u003eDeepa, D. and A. 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Jacobsen, \u003cem\u003eScalable deep learning on distributed infrastructures: Challenges, techniques, and tools.\u003c/em\u003e ACM Computing Surveys (CSUR), 2020. \u003cstrong\u003e53\u003c/strong\u003e(1): p. 1\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eZhou, Y., et al. \u003cem\u003eTensorRT Implementations of Model Quantization on Edge SoC\u003c/em\u003e. in \u003cem\u003e2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)\u003c/em\u003e. 2023. IEEE.\u003c/li\u003e\n\u003cli\u003eTamagusko, T., M. Gomes Correia, and A. Ferreira, \u003cem\u003eMachine Learning Applications in Road Pavement \u003c/em\u003eManagement: A Review, Challenges and Future Directions. Infrastructures, 2024. 9(12).\u003c/li\u003e\n\u003cli\u003eTian, X., et al., Garbage classification algorithm based on improved mobilenetv3. IEEE Access, 2024.\u003c/li\u003e\n\u003cli\u003ehttps://www.kaggle.com/datasets/patelmihir/road-defects-nonaugmented\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"iranian-journal-of-science-and-technology-transactions-of-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"istc","sideBox":"Learn more about [Iranian Journal of Science and Technology, Transactions of Civil Engineering](http://link.springer.com/journal/40996)","snPcode":"40996","submissionUrl":"https://submission.nature.com/new-submission/40996/3","title":"Iranian Journal of Science and Technology, Transactions of Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Road Defect Detection, EfficientNetV2-B0, CBAM, Edge AI, Real-Time Vision, Deep Learning Algorithms, Proactive Maintenance Strategies","lastPublishedDoi":"10.21203/rs.3.rs-7150970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7150970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRoad defect detection is essential to road maintenance and road safety, but current approaches barely achieve the desired accuracy and real-time processing. This work introduces a novel hybrid deep learning architecture that leverages EfficientNetV2-B0 together with Convolutional Block Attention Module (CBAM) in order to achieve high-precision, real-time multi-class road defect detection. The system leverages EfficientNetV2-B0's strong feature extraction and complements it with CBAM's attention mechanism for focusing on important defect regions to improve detection accuracy while maintaining computation efficiency. We tested the system on a well-chosen dataset that contains 1200 images for four classes of defects (cracks, potholes, patches, and surface defects), with better performance at 97% accuracy and 21ms inference per image using GPU hardware. Comparative experiments show our hybrid approach outperforms individual CNNs (EfficientNetV2-B0: 93.5%) and Vision Transformers (ViT-Tiny: 97.1% but 70ms latency) in speed-accuracy trade-offs. The high performance of the system is further augmented by the 6th October City- Giza- Egypt case study in which it precisely recognized and classified important pavement distresses in real urban environments, such as fine cracks (98% accuracy), hazardous potholes (96% recall), and complex surface defects (97% precision). The suggested system has a high degree of technical advantage for infrastructure monitoring applications, with real-time processing capabilities (21.5ms per image) and low computational overheads (1.42\u0026nbsp;billion FLOPs). This work encourages automated monitoring of infrastructure by providing a scalable, high-accuracy, and low-latency solution for road defect detection.\u003c/p\u003e","manuscriptTitle":"Urban Road Defect Detection: A Hybrid EfficientNetV2-B0 and CBAM Framework with Real-Time Computer Vision Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 08:49:01","doi":"10.21203/rs.3.rs-7150970/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T03:03:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T05:10:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223699261865679469305400893059523137997","date":"2025-08-19T04:51:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T03:44:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T12:25:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T12:24:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Iranian Journal of Science and Technology, Transactions of Civil Engineering","date":"2025-07-17T16:09:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"iranian-journal-of-science-and-technology-transactions-of-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"istc","sideBox":"Learn more about [Iranian Journal of Science and Technology, Transactions of Civil Engineering](http://link.springer.com/journal/40996)","snPcode":"40996","submissionUrl":"https://submission.nature.com/new-submission/40996/3","title":"Iranian Journal of Science and Technology, Transactions of Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"78a99fed-165f-4faf-9736-0fed1dbee316","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-06T19:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 08:49:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7150970","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7150970","identity":"rs-7150970","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}