An Optimized Deep Learning Framework for Early Detection and Prevention of Forest Fires Using Advanced Training Techniques

An Optimized Deep Learning Framework for Early Detection and Prevention of Forest Fires Using Advanced Training Techniques | 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 An Optimized Deep Learning Framework for Early Detection and Prevention of Forest Fires Using Advanced Training Techniques D Ranjani, S Santhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6734052/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Forests are indispensable ecosystems that sustain biodiversity, regulate the global climate, and provide essential resources for life on Earth. However, these critical habitats are increasingly jeopardized by forest fires, a major environmental challenge that leads to severe ecological, economic, and social repercussions. Forest fires contribute to the destruction of flora and fauna, exacerbate greenhouse gas emissions, and disrupt the ecological equilibrium. To mitigate such disasters, early detection systems are crucial for enabling rapid response, minimizing damage, and preserving these vital ecosystems. This research presents a novel forest fire detection system utilizing YOLO v11 (You Only Look Once) deep learning model. YOLO, recognized for its real-time object identification proficiency, is further refined in this version and addresses the specific issues associated with detecting forest fires. The suggested technique emphasizes early detection by precisely identifying fire patterns in images or videos, regardless of adverse environmental conditions that include inadequate lighting, dense smoke, or obstructions. The methodology incorporates advanced deep learning techniques, optimized network architectures, and a comprehensive dataset comprising diverse scenarios of forest fire outbreaks. By training YOLO v11 model on annotated datasets, the system achieves high precision and recall rates, ensuring minimal false positives and negatives. This approach enables rapid identification and localization of fire hotspots, facilitating immediate intervention to contain and extinguish fires. Integration of this model into forest fire management systems provides a robust tool for real-time monitoring and decision-making. It can be deployed through aerial surveillance using drones, fixed camera installations, or satellite imagery analysis, offering scalability and adaptability for different environments. Additionally, the system supports predictive modeling to analyze fire spread patterns, enhancing proactive measures for disaster prevention. Implementation of this advanced detection system represents a significant leap in forest conservation and disaster management efforts. It underscores the potential of artificial intelligence and deep learning(DL) in addressing global environmental challenges, safeguarding biodiversity, and promoting sustainable development. This project ensures ecological and economic stability in the face of climate change by reducing forest fire damage. forest fire YOLO YOLOv11n Convolutional Neural Networks. Dataset epoch Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Forest fires, among the most destructive natural disasters, cause severe harm to environment, economy, and society. They can cause property damage and human casualties, as well as the loss of biodiversity and climate change due to high carbon dioxide emissions. Forest fires are anticipated to increase in frequency and intensity due to increasing global temperatures and increasingly unpredictable weather patterns. This increasing danger emphasizes how urgently efficient early detection technologies are needed to lessen the effects of catastrophic disasters. Forests, as essential elements of terrestrial ecosystems, provide a diverse array of forestry products and are pivotal in sustaining ecological equilibrium, delivering substantial benefits to economy and environment. Forest fires are substantial natural disasters that threaten the integrity of forest ecosystems, lead to the depletion of global forest resources, that cause injuries to individuals. Researching early forest fire identification is essential. Due to the abundance of oxygen in forests and the rapid occurrence of air convection, wildfires propagate rapidly. Worst natural disasters, forest fires, damage the environment, economy, and civilization. The initial method of detecting forest fires was manual examination, which had a low efficiency and large costs associated with both human and material resources. While there are benefits to using conventional techniques for identifying forest fires, such as satellite surveillance, infrared sensors, and ground patrols, there are also important drawbacks. Although satellite imaging offers wide coverage, there are considerable lags between image captures due to its poor temporal resolution. Although they are good at identifying heat signatures, infrared sensors need a lot of upkeep and might be hampered by cloud cover. Ground patrols may only cover a small region and need a lot of resources. These drawbacks emphasize the necessity of increasingly sophisticated, real-time detection systems. Thus, sensor-based detection quickly replaced manual exams. Sensor-based detection system excels in restricted areas. A variety of sensors, including temperature, humidity, smoke, gas, and integrated sensors, are used to detect forest fires. Its installation costs are very high, its detecting range is limited, and it has challenging power supply networking and communication issues. Furthermore, the sensors are unable to deliver crucial visual data that would enable firefighters to quickly assess the condition at the fire scene. Satellite remote sensing is impacted by cloud cover and the weather. Sensor-based detection replaced manual exams quickly. In tight spaces, the sensor-based detection system works well. Satellites fail to recognize forest fires in real time and lack temporal and spatial resolution. Compared to satellite photos, the camera performs better in real-time as a sensor. It could detect forest fires in remote forests when mounted on a drone. In contrast to non-imaging sensors, including smoke alarms, cameras provide firefighters with a more detailed visual representation of forest fire regions. Researchers are beginning to apply digital image processing technology for forest fire detection as a result of the advancements in computer vision. Forest fires are very destructive and unpredictable. There are clear drawbacks to the conventional approaches of computer vision detection, sensor-based detection, satellite remote sensing, and manual inspection. Characteristics of forest fires could be learned and adaptively obtained through DL algorithms. However, satellites are still unable to combine temporal and spatial resolution for identifying forest fires in real-time. The fire detection system is important in today's scenario to prevent fire in dense forest areas to protect wildlife animals and nature’s resources. The main difficulty in detecting forest fires is striking a balance between speed and precision. For prompt intervention to avoid false alarms that might put a burden on resources and lead to complacency, early and accurate detection is essential. Particularly in big, rural locations that require prompt answers, current systems frequently fall short. Promising solutions are provided by deep learning and computer vision technologies, especially object identification models like the YOLO family. Because YOLO models are rapid, accurate, that have been effectively applied to a range of real-time detection tasks, they are ideal for detecting forest fires. DL concepts along with an efficient object detection algorithm such as YOLO v11[26], the forest fire can be detected in an earlier stage. 2. Literature Review The study "A Multimodal DL Approach for Real-Time Fire Detection in Aerial Imagery" presents a multimodal strategy that improves the model's efficacy relative to conventional (convolutional neural network) CNN techniques. Multimodal architecture attained true positive rate (TPR) of 100% and false positive rate(FPR) of 0%, whereas optimal single-stream CNN produced TPR97% and an FPR0.02% [1]. "YOLOv10: Real-Time End-to-End Object Detection" illustrates that YOLOv10 attains exceptional performance and minimal latency relative to other advanced detectors, establishing its superiority. [2]. "Image fire detection algorithms based on convolutional neural network" presents an innovative fire detection methodology based on CNN architectures, including Faster R-CNN, R-FCN, SSD, and YOLO V3, attaining an accuracy of 83% [3]. "False positive decrement research for fire and smoke detection in surveillance cameras using spatial and temporal features based on deep learning" offers a solution for reducing false positive detections through novel smoke detection algorithm that integrates spatial and temporal features based on DL [5]. "Saliency detection and deep learning-based wildfire identification in UAV imagery" introduces an innovative saliency detection technique designed to swiftly identify and segment primary fire zones in aerial photos utilizing Fire-Net, a 15-layer self-learning model [6]. “ A deep learning-based object identification system for forest fire detection.” proposes DL-based smoke detection system utilizing RetinaNet and Faster R-CNN, with optimal results attained through Faster R-CNN, identifying smoke columns with approximately 80% F1-score and 90% detection rate. System has been verified utilizing HPWREN dataset, with an average detection time of 6.3 minutes from initiation of fire[30]."Video-based fire detection using deep learning models" presents DL approach for fire detection utilizing video sequences, employing Faster R-CNN to distinguish between fire and non-fire areas based on spatial characteristics [7]. "Analysis of machine learning methods for wildlife security monitoring with unmanned aerial vehicles" assesses ML and DL methodologies including Haar, LBP cascades, Faster R-CNN, SSD, and YOLO, determining that YOLO is most effective algorithm for this task [4]. “EfficientDet: Scalable and Efficient Object Detection” EfficientDet represents a series of object detectors that emphasize efficiency through the application of a unique BiFPN for feature fusion and a compound scaling approach to uniformly adjust all network components. This results in superior accuracy with substantially fewer parameters and computations relative to previous detectors.[20]."Advanced Forest Fire Detection Using YOLOv10: A Deep Learning Approach" Outlines a real-time forest fire detection system utilizing YOLOv10 models—namely "YOLOv10n, YOLOv10s, YOLOv10m, and YOLOv10b"—to capitalize on advanced object detection capabilities, hence improving precision and velocity for prompt fire detection. Research employs publicly accessible forest fire image dataset for training and assessment, analyzing efficacy of YOLOv10 variants—"YOLOv10n (nano), YOLOv10s (small), YOLOv10m (medium), and YOLOv10b (balanced)"—in forest fire detection [27]. "YOLOv11: An Overview of the Key Architectural Enhancements" emphasizes enhancements in YOLOv11, that enhance accuracy and processing velocity while minimizing requisite number of parameters, making it appropriate for diverse applications ranging from edge computing to cloud-based analysis [26]. Analysis of several fire detection techniques, including YOLO, R-CNN, EfficientDet, R-FCN, SSD, and RetinaNet, indicates that YOLO is most effective object detection algorithm. In light of these findings, we propose the YOLOv11n framework for detecting forest fires. 3. Comparative Analysis of Object Detection Models for Fire Recognition Object detection is fundamental to computer vision, allowing machines to recognize and highlight objects in images or videos. Over the years, several state-of-the-art algorithms have been developed, each with unique strengths and trade-offs. This section compares five prominent object detection models: YOLO[26], EfficientDet[20], RetinaNet[30], Faster R-CNN[4], and Mask R-CNN[31]. YOLO is distinguished for its real-time object identification skills, utilizing a singular CNN to directly predicted bounding boxes and class probabilities, resulting in remarkable speed. Recent versions, including YOLOv11, utilize advanced methodologies including transformers and attention mechanisms to enhance precision. EfficientDet balances speed and accuracy by leveraging EfficientNet as its backbone and employing BiFPN for feature fusion, achieving high accuracy with reduced computational complexity. RetinaNet reduces the disparity among foreground and background classes by a focal loss function, offering an effective balance between speed and accuracy, hence rendering it appropriate for detection of small and densely packed objects. Dual-stage detector known as Faster R-CNN first generates region recommendations prior to classifying them. While computationally intensive, it delivers high accuracy, particularly for complex scenes with overlapping objects. An improvement on "Faster R-CNN, Mask R-CNN" has a branch for pixel-level segmentation masks, thereby making it appropriate for applications that require instance segmentation and object detection. Table 3.1 Comparison of Deep Learning Algorithms Algorithm Accuracy Inference Time (ms/frame) Key Strengths YOLOv11 94% 12 Real-time speed and high accuracy EfficientDet-D7 92% 50 Balance of accuracy and computational efficiency RetinaNet 91% 45 Excellent for small and densely packed objects Faster R-CNN 93% 150 High accuracy in complex scenes Mask R-CNN 93% 200 Instance segmentation and detailed analysis The performance of these algorithms varies significantly based on their design and intended applications. With an impressive 94% accuracy rate and an inference time of approximately 12 milliseconds per frame, YOLOv11 is appropriate for real-time applications consisting of autonomous vehicles and surveillance. EfficientDet-D7 achieves 92% accuracy with an inference time of 50 milliseconds per frame, effectively balancing accuracy and processing efficiency. RetinaNet attains 91% accuracy and an inference duration of 45 milliseconds per frame, demonstrating superior performance in contexts including small and densely clustered objects. Faster R-CNN achieves 93% accuracy but requires 150 milliseconds per frame, making it suitable for high-accuracy tasks in complex scenes. Mask R-CNN achieves 93% accuracy comparable to Faster R-CNN while enhancing its functionality to include instance segmentation, with an inference time of 200 milliseconds per frame, making it suitable for offline processing and applications requiring meticulous pixel-level analysis. Every algorithm has advantages, and specific requirements of application determine that model is appropriate. YOLO’s speed, EfficientDet’s balance, RetinaNet’s specialization, and the precision of Faster and Mask R-CNNs cater to diverse needs in object detection tasks. By comprehending these trade-offs, practitioners may determine the most appropriate model for their application. 4. Comparative Study of YOLO Versions for Forest Fire Detection YOLO has improved object-detecting performance with each release, addressing previous limitations. This section compares YOLO versions v1–v11's architectural improvements and forest fire detection performance with minimal training epochs. 4.1 Evolution of YOLO Versions YOLOv1 introduced a grid-based detection system, unifying the tasks of object localization and classification in a single CNN. Despite its innovative approach, it struggled with small object detection and produced higher false positive rates. Anchor boxes and multi-scale training improved YOLOv2's ability to recognize objects of various sizes. YOLOv3 further refined detection accuracy with the introduction of Darknet-53 as its backbone and multi-scale predictions, enhancing feature representation across layers. Subsequent versions continued to build on these foundations. YOLOv4 integrated CSPNet and PANet to improve feature aggregation and spatial attention, resulting in higher accuracy. YOLOv5 prioritized model efficiency, achieving comparable performance with reduced computational overhead. Dynamic and resource-constrained contexts benefit from YOLOv6's performance and false positive reduction enhancements. YOLOv7 emphasized real-time processing through extended efficient layer aggregation networks, allowing rapid detection without compromising accuracy. YOLOv8 implemented dynamic anchor assignments and improved model scaling, providing greater adaptability for complex datasets Further advancements included YOLOv9's adaptive learning rate schedulers and its heightened precision in challenging scenarios. YOLOv10 introduced transformer-based layers, improving contextual understanding and detection in cluttered scenes. Finally, YOLOv11 incorporated advanced scaled transformers and attention mechanisms, achieving the highest accuracy and computational efficiency among all versions. 4.2 Performance Metrics Over 20,000 Roboflow annotated forest fire images have been utilized for assessing YOLO versions. On "training (70%), validation (20%), and testing (10%)" subsets of dataset, each model had been trained for 10 epochs on "Google Colab utilizing an NVIDIA Tesla T4 GPU". Precision, recall, and mAP have been employed for evaluating each version. Results are in table below: Table 4.1 Yolo Version comparison YOLO Version Precision Recall mAP (%) Training Time (Epochs) YOLOv1 75% 70% 72.5 10 YOLOv2 80% 75% 77.5 10 YOLOv3 85% 80% 82.5 10 YOLOv4 88% 85% 86.5 10 YOLOv5 90% 88% 89 10 YOLOv6 91% 89% 90 10 YOLOv7 92% 90% 91 10 YOLOv8 93% 91% 92 10 YOLOv9 93.5% 92% 92.5 10 YOLOv10 94% 92.5% 93 10 YOLOv11 95% 93% 94 10 4.3 Analysis and Implications Most effective model had been YOLOv11, that achieved 95%precision and 94%mAP with minimal training epochs. Advanced backbone networks and attention techniques enabled it detect forest fires in challenging conditions. These results demonstrate that architectural improvements improve detection accuracy and robustness. Results demonstrate that contemporary YOLO designs are suitable for detecting forest fires in real-time. Due to its streamlined processing and stable performance, YOLOv11 could be employed in crucial applications for early fire detection and mitigation. As forest fires become more prevalent and intense owing to climate change, integrating advanced AI-based systems into monitoring frameworks might improve response times and resource allocation, protecting ecosystems and humans. 5. Proposed Work Fire incident is a serious concern in forest areas which destroy natural resources human life and also wildlife. The proposed research work aims at detecting the fire/smoke at the earlier stage and alarms the forest officer to take remedial measures. The proposed framework aims at detecting the fire using YOLOv11 with modification. Joseph Redmon's YOLO algorithm advances real-time object detection. Despite other approaches, YOLO considers object detection as a regression job that predicts bounding boxes and class probabilities from complete images. This novel method accelerates detection speed. The most recent version, YOLOv11, builds upon its predecessors by incorporating enhanced architecture and advanced training techniques. By adding few more changes to YOLOv11 framework more accuracy can be obtained. Figure 3 illustrates scenario of forest fires. 5.1 YOLO v11 The YOLO 11 appears to be the best model AI for faster computations and comprehending images, even the smallest details with best resolutions. The latest YOLO version is offering up to 10% accuracy increase compared to previous iterations, something that may seem like a minor improvement but it’s enough for the majority of practical application cases. Table 5.1 Supported Tasks and Modes [32] The introduction of a single neural network architecture incorporating both bounding box regression and object classification within the YOLO framework has fundamentally altered the object detection paradigm [17]. The entire paradigm of approaches aimed at solving the problem of detection was sharply modified as the traditional two-stage methodologies were complemented by the possibility of end-to-end training of the network due to the fully differentiable architecture. Generally, the YOLO architecture is made up of three basic building blocks. The first and foremost is the backbone which is the basic feature extraction layer that applies CNNs to process input images into multiple feature maps at different scales. The second is the neck which is designed as a connection structure and functions as an intermediate processing element that uses combination layers to integrate feature representations at different scales and refine them. The third is the head which is the output block that serves the purpose of predicting the location and the class of objects apparent in an image using the refined feature maps as inputs. 5.2 YOLOv11 Algorithm YOLOv11 is a real-time object detection system that improves on its predecessors. It employs an improved backbone architecture, such as CSPNet, for efficient feature extraction, and incorporates multi-scale detection through feature pyramid and path aggregation networks for handling objects of varying sizes. Attention mechanisms, like SE blocks or CBAM[24], are integrated to focus on critical regions, reducing false detections. YOLOv11 optimizes anchor box design using clustering algorithms tailored to specific datasets, enhancing bounding box accuracy. To improve computational efficiency, it utilizes depthwise separable convolutions, while a refined loss function balances classification, localization, and confidence scoring. Generalization is strengthened by advanced data augmentation methods including MixUp and Mosaic, resulting in it being resistant to a variety of circumstances. With real-time inference capabilities and high detection accuracy, YOLOv11 is particularly suited for time-critical tasks like forest fire detection, ensuring swift response and effective resource protection. 5.3 YOLO v11 Architecture With several architectural improvements intended to increase computing efficiency without compromising accuracy, YOLO11 launched in September 2024. This version introduces innovative components such as the C3k2 block and the C2PSA block, which facilitate better feature extraction and processing capabilities. Consequently, it achieves slightly improved performance with significantly fewer parameters in the model. The following are the key features of YOLO11. C3k2 Block: A less computationally demanding variant of Cross Stage Partial (CSP) Bottleneck, C3k2 block is a recent addition to YOLO11. It reduces processing time by replacing C2f block in neck and backbone with 2 smaller convolutions rather than a single large convolution. C2PSA Block: C2PSA block, which follows "Spatial Pyramid Pooling–Fast (SPPF)" block, enhances spatial attention through "Cross Stage Partial with Spatial Attention mechanism". Improved detection accuracy may result from model's ability to concentrate more efficiently on significant areas of image due to this attention feature. Table 5.2 Performance Metrics[33] 6. Experiment Analysis 6.1 Datasets The efficacy of any ML model, especially in computer vision, is greatly influenced by quality and diversity of datasets employed for training and assessment. For current research, we employed a publicly available dataset of images from forest fires from several online repositories. Images from various viewpoints and circumstances, including aerial and ground-level photographs, are included in the dataset. 6.2 Data Set Collection We collected an extensive dataset from Roboflow for evaluating detection algorithm's performance[28]. This dataset consists of 17,000 image samples demonstrating smoke and fire. It mostly includes images of forest fires, with a smaller number of images of urban fires that depict incidents of varying scales. This dataset's diversity improves algorithm's capacity in generalizing across various fire-related situations. Training, validation, and test sets have been produced from dataset. 6.3 Experimental Environment and Procedures For current research, we applied Google Colab[29] in experiment with T4 GPU and coding using Python. After performing the object detection using YOLOv11 the following results were obtained. YOLOv11 object detection mechanism detects the fire from the trained dataset. Real-time dataset collected from normal camera was also detected with higher accuracy. Due to overfitting the epoch was limited to 20. In YOLOv11 we chose the nano version. YOLOv11n (nano version) represents a compact and efficient iteration of YOLOv11, tailored for edge devices or scenarios that demand lower computational resources while still delivering exceptional performance in object detection tasks.YOLOv11n has been fine-tuned to possess fewer parameters compared to its larger counterparts (such as YOLOv11 or YOLOv11x). This optimization leads to decreased memory usage and faster inference times, making it particularly suitable for mobile devices, embedded systems, and real-time applications like drones and surveillance systems. Despite its reduced architecture, YOLOv11n can achieve processing speeds of up to 60 frames per second, which is essential for applications where timely responses are critical. Although it is smaller in size, YOLOv11n retains high level of accuracy for object detection, employing optimized feature extraction techniques to identify small and occluded objects, akin to its larger versions. The streamlined model size allows it to operate effectively on devices with limited processing capabilities, striking a favorable balance between speed and accuracy. Therefore, for those engaged with edge devices or in need of a lightweight model for real-time detection, YOLOv11n is an excellent option due to its rapid inference capabilities and reduced memory requirements, all while maintaining robust detection performance. Datasets perform crucial functions. In the proposed technique various datasets were used out of which, large dataset with minimum epochs gives higher accuracy compared to medium-sized dataset with higher epochs such as 50 or more. Thus we conclude that our paper using yolov11n shows higher accuracy similar to yolov11x which requires more computational resources, with the yolov11n version we can get higher accuracy with minimum resources like GPU and also increases the FPS speed since it is the nano version. Thus if we want to deploy model on edge devices mobile devices and drones, YOLOv11n can be used provided all the considerations are made. 6.4 Predictive Model Evaluation Precision Precision measures proportion of expected positive cases that are TP. High Precision: demonstrates that majority of positive predictions are accurate, indicating a low rate of FP. " P = (TP) / (TP + FP)" Recall (Sensitivity or True Positive Rate) Recall quantifies percentage of TP cases that are accurately detected. A low percentage of FNs is demonstrated by a high recall, indicating that model is detecting majority of TP. R = (TP)/(TP + FN) Accuracy "A=(TP+TN)/(TP+TN+FP+FN)" Where: A= Accuracy TP = True Positives TN = True Negatives FP = False Positives FN = False Negatives Where: (True Positives)TP: Correctly predicted objects (i.e., class and bounding box are correct). (False Positives)FP: Incorrectly predicted objects (i.e., the class is wrong or the bounding box doesn't match). True Negatives (TN): Correctly predicted negative instances (not often used in object detection tasks, as negative samples are typically not explicitly considered). False Negatives (FN): Objects that were not detected but should have been. Key Steps in the Implementation 1. Environment Setup i. Required libraries, including ultralytics for YOLO and roboflow, are installed. ii. Roboflow is used to manage and download the dataset. 2. Dataset Preparation i. The dataset is accessed via Roboflow using an API key. ii. The FireMix project from the workspace is selected, and version 9 of the dataset is downloaded in YOLOv11 format. 3. Model Initialization i. A pre-trained YOLOv11 model is loaded. Various configurations of the model (e.g., n, s, m, l, x) can be utilized depending on the specific requirements. 4. Model Training i. data.yaml file contains custom dataset that is employed to train YOLOv11 model. ii. 640x640 pixel input image has been employed for training over 20 epochs. 5. Output i. After training, model is expected to produce a robust system capable of identifying forest fires from images. 7. Results 7.1 Precision Confidence Curve A Precision Confidence Curve is a visualization used to evaluate a model's performance by showing how confident predictions relate to precision. It provides insights into the confidence threshold's impact on precision and helps fine-tune the threshold for specific applications. Model's performance for Fireresistances class is thoroughly examined employing Precision-Recall (PR) curve displayed in Fig. 7.1 . This graph illustrates that recall (sensitivity) and precision (positive predictive value) get traded off at different classification thresholds. At an Intersection over Union (IoU) threshold of 0.5, the model's mAP is 0.939. When PR is balanced, this statistic demonstrates a high degree of accuracy in identifying the target class. With few FP at higher thresholds, model appears to be performing extraordinarily well for high-confidence predictions, as indicated by a significant decrease in precision near end of curve. The close alignment between the Fireresistances class-specific curve and the overall mAP curve reflects the consistency and robustness of the model in identifying this particular class across the dataset. Applications requiring accurate categorization of fire-resistance data depend on the model's ability to manage complicated scenarios, that is demonstrated by its high mAP score. 7.2 Confusion Matrix A device for evaluating a model's performance is confusion matrix. It provides a thorough analysis of the model's performance, enabling users in computing crucial parameters including recall and precision. 7.2.1 Confusion Matrix Analysis Confusion matrix for model's classification performance on Fireresistances class appears in Fig. 7.2 . The confusion matrix provides a detailed breakdown of the model’s predictions, highlighting the distribution of TP, FP, TN, and FN. TP (3704): 3704 instances of the Fire resistance class had been accurately identified by model. FP (672): 672 occurrences of the background class had been misclassified as Fireresistances by model.TN (N/A): If total counts have been determined, it could infer number of correctly detected background occurrences, even though it isn't explicitly displayed. FN (255): model misclassified 255 instances of the Fireresistances class as background since it failed to detect them. With a high number of TP and a comparatively low FN, confusion matrix depicts that model performs well in classification. FP, while higher in count, are within acceptable limits, indicating a moderate level of misclassification between background and Fireresistances. Matrix supports model's potential for real-world application in situations requiring precise fire-resistance classification by graphically highlighting the degree to which it separates the two groups. 7.3 Training the batch in YOLOv11 Figure 7.3 and 7.4 illustrate detection results of the fire classification model applied to diverse set of test images. Each image demonstrates model's ability in localizing fire regions effectively across varying contexts, including urban fires, forest fires, and controlled flames. Bounding Boxes : The blue bounding boxes indicate regions identified as fire by the model. Each box corresponds to a detected fire instance with high confidence. Diversity in Contexts : The model exhibits strong detection skills in a variety of scales and lighting situations, in addition to a range of environmental variables, including open fields, dense forests, and urban settings. 7.4 Overall results Figure below displays overall YOLO v11n object detection model results on dataset. 7.4.1 Interpretation Based on the Graphs: Training Loss Metrics : Table 7.1 Training Loss Metrics Validation Loss Metrics : Metric Approximate Starting Value Approximate Ending Value Interpretation train/box_loss ~ 1.8 ~ 1.0 The decreasing trend indicates the model is improving at localizing object bounding boxes. train/cls_loss ~ 1.8 ~ 0.6 The classification error is reducing, meaning the model is better at differentiating object classes. train/dfl_loss ~ 1.6 ~ 1.0 The model's confidence in accurate bounding box predictions is improving. Table 7.2 Validation Loss Metrics: Performance Metrics : Metric Approximate Starting Value Approximate Ending Value Interpretation val/box_loss ~ 1.8 ~ 1.1 The decreasing validation box loss suggests the model is generalizing well. val/cls_loss ~ 1.6 ~ 0.6 Classification loss is improving, indicating better validation accuracy. val/dfl_loss ~ 1.5 ~ 1.0 A steady decrease reflects improved performance on unseen data. Table 7.3 Performance Metrics Metric Approximate Starting Value Approximate Ending Value Interpretation metrics/precision(B) ~ 0.6 ~ 0.9 Precision is increasing, meaning fewer FP. metrics/recall(B) ~ 0.6 ~ 0.9 Higher recall indicates better object detection coverage. metrics/mAP50(B) ~ 0.5 ~ 0.9 Model's 50% IoU detection performance is improving. metrics/mAP50-95(B) ~ 0.3 ~ 0.75 Model performance is strong when IoU thresholds increase. The analysis of the training and validation performance metrics reveals several key observations and research insights. Initially, the model demonstrates learning without overfitting, as evidenced by the steady decline in training and validation losses, including "box loss, classification loss, and distribution focal loss". Model appears to be well-regularized, ensuring strong generalization to unknown data, as evidenced by the slight difference among training and validation losses. Second, model's improved capacity to precisely recognize objects while minimizing FP is demonstrated by performance metrics including precision and recall, indicating a consistent improvement. The model's improving capacity for determining objects with more precision and consistency is further supported by increasing trend in mAP metrics across different IoU thresholds. Lastly, the presence of smooth trend lines, represented by the orange dotted lines in the plots, indicates that the model's learning process is stable across epochs without erratic fluctuations, signifying a well-converged training process. Collectively, these observations demonstrate reliability and efficiency of model at object-detecting tasks. 8. Conclusion The current research concludes by highlighting the efficient application of YOLO v11n model for forest fire detection, proving its capacity to provide prompt and precise fire identification. The research utilized Google Colab with T4 GPU for coding in Python, which facilitated robust experimentation. The YOLO v11 object detection mechanism successfully identified fire from a trained dataset, and real-time data collected from standard cameras exhibited high accuracy levels. For reducing overfitting, training has been limited to 20epochs, ensuring optimal performance. YOLOv11n nano version has been selected since it is small and efficient for edge devices and lower computational resource applications. YOLOv11n can manage 60 frames per second, therefore being appropriate for real-time applications including drones and surveillance systems despite its reduced architecture. The enhanced feature extraction approaches of this model could recognize small and obstructed objects in addition to larger models while using less memory. The study also emphasizes the critical role of dataset selection, revealing that using a large dataset with fewer epochs yields higher accuracy compared to smaller datasets with more epochs. Consequently, the YOLOv11n version achieves impressive accuracy comparable to the larger YOLOv11x model, but with far lower resource requirements. It balances speed and accuracy, thus making it ideal for edge devices, mobile platforms, and drones. Overall, this research underscores the importance of innovative technologies in forest fire detection and highlights the potential for collaboration with environmental scientists and fire management. Declarations Funding Declaration NA References Posch, J. R., Daniel, A. (2019). A multimodal deep learning approach for real-time fire detection in aerial imagery. Chalmers University Of Technology . Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. Tsinghua University . Li, P., Zhao, W. (2020). Image fire detection algorithms based on convolutional neural networks. Science Direct Elsevier, Volume 19, June 2020 . Alexandrov, D., Pertseva, E., Berman, I., Pantiukhin, I., Kapitonov, A. (n.d.). Analysis of Machine Learning Methods for Wildfire Security Monitoring with Unmanned Aerial Vehicles. Proceeding of The 24th Conference of Fruct Association . Lee, Y., Shim, J. (2019). False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning. Electronics, 15 October 2019 . Zhao, Y., Ma, J., Li, X., Zhang, J. (2018). Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. Sensors, 27 February 2018 . Kim, B., Lee, J. (2019). A Video-Based Fire Detection Using Deep Learning Models. Applied Science, 18 July 2019 . Wu, L., Chen, L., Hao, X. (2021). Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network. Information 2021, 12, 59 . Solórzano, A., Eichmann, J., Fernández, L., Ziems, B., Jiménez-Soto, J. M., Marco, S., Fonollosa, J. (2022). Early fire detection based on gas sensor arrays: Multivariate calibration and validation. Sens. Actuators B Chem., 352, 130961 . Zheng, S., Gao, P., Wang, W., Zou, X. (2022). A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network. Appl. Sci. 2022, 12, 6721 . Rui, X., Li, Z., Zhang, X., Li, Z., Song, W. (n.d.). A RGB-Thermal based adaptive modality learning network for day–night wildfire identification. Bouguettaya, A., Zarzour, H., Taberkit, A. M., Kechida, A. (2022). A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. Signal Process . Li, Y., Zhang, W., Liu, Y., Jing, R., Liu, C. (2022). An efficient fire and smoke detection algorithm based on an end-to-end structured network. Eng. Appl. Artif. Intell., 116, 105492 . Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. Comput. Vis. ECCV 2020, 2020, 213–229 . Qin, Y. Y., Cao, J. T., Ji, X. F. (2021). Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3. Int. J. Autom. Comput., 18, 300–310 . Redmon, J., Farhadi, A. (2018). Yolov3: An Incremental Improvement. arXiv 2018, arXiv:1804.02767 . Wang, Y., Hua, C., Ding, W., Wu, R. (2022). Real-time detection of flame and smoke using an improved YOLOv4 network. Signal Image Video Process., 16, 1109–1116 . Howard, A., Sandler, M., Chu, G., Chen, L. C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al. (2019). Searching for MobileNetV3. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019, pp. 1314–1324 . Bochkovskiy, A., Wang, C. Y., Liao, H. Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv 2020, arXiv:2004.10934 . Tan, M., Pang, R., Le, Q. V. (2020). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14–19 June 2020, pp. 10781–10790 . Bahhar, C., Ksibi, A., Ayadi, M., Jamjoom, M. M., Ullah, Z., Soufiene, B. O., Sakli, H. (2023). Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN. Electronics 2023, 12, 228 . Yu, S., Sun, C., Wang, X., Li, B. (2022). Forest fire detection algorithm based on Improved YOLOv5. J. Phys. Conf. Ser., 2384, 012046 . Dou, Z., Zhou, H., Liu, Z., Hu, Y., Wang, P., Zhang, J., Wang, Q., Chen, L., Diao, X., Li, J. (2023). An Improved YOLOv5s Fire Detection Model. Fire Technol. Woo, S., Park, J., Lee, J. Y., Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018, pp. 3–19 . Du, J. (2018). Understanding of object detection based on cnn family and yolo. Journal of Physics: Conference Series, volume 1004, page 012029. IOP Publishing . Khanam, R., Hussain, M. (2024). Yolov11: An Overview Of The Key Architectural Enhancements. arXiv:2410.17725, October 2024 . Patel, S. H., Patel, D. H., Bankar, N. M., Chaudhari, N. K., Dave, U. R., Prajapati, N. K. (2024). Advanced Forest Fire Detection Using YOLOv10: A Deep Learning Approach. J. Electrical Systems, 20-3, 4115-4125 . Roboflow. Retrieved from https://universe.roboflow.com/. Google Colab. Retrieved from https://colab.research.google.com/. Guede-Fernández, F., Martins, L., de Almeida, R. V., Gamboa, H., & Vieira, P. (2021). A deep learning-based object identification system for forest fire detection. Fire, 4(4), 75. Jahagirdar, A., Sathe, N., Thorat, S., Saxena, S. (n.d.). Fire detection in nano-satellite imagery using Mask R-CNN. International Journal of Signal and Imaging Systems Engineering . https://docs.ultralytics.com/models/yolo11/#supported-tasks-and-modes https://docs.ultralytics.com/models/yolo11/#performance-metrics Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6734052","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463757698,"identity":"dd8981b4-0945-4a87-9a6d-f21c9f748b46","order_by":0,"name":"D Ranjani","email":"data:image/png;base64,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","orcid":"","institution":"KIT- KalaignarKarunanidhi Institute Of Technology","correspondingAuthor":true,"prefix":"","firstName":"D","middleName":"","lastName":"Ranjani","suffix":""},{"id":463757699,"identity":"bb0c53ee-efde-4e1a-994e-d34615c31eff","order_by":1,"name":"S Santhi","email":"","orcid":"","institution":"KIT- KalaignarKarunanidhi Institute Of Technology","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Santhi","suffix":""}],"badges":[],"createdAt":"2025-05-23 15:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6734052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6734052/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83815109,"identity":"d1eff319-305e-4534-914f-37a679304529","added_by":"auto","created_at":"2025-06-03 07:38:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":407010,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5.1 Scenario of forest fire.\u003c/p\u003e","description":"","filename":"5.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/938c2e6cc844dadee7e7eae5.png"},{"id":83815110,"identity":"41fb0996-cdb7-4a84-8dec-e34a216e650f","added_by":"auto","created_at":"2025-06-03 07:38:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150018,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5.2 YOLO v11 evolution\u003c/p\u003e","description":"","filename":"5.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/cf2e84149477282a9bff1b95.png"},{"id":83817236,"identity":"ea400423-c8e0-44ac-8666-b0afd300d24a","added_by":"auto","created_at":"2025-06-03 07:54:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":127484,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5.3 YOLO v11 Architecture\u003c/p\u003e","description":"","filename":"5.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/5052ffa632f8c3781d34583a.png"},{"id":83816099,"identity":"8f1c36a0-bfd6-4279-bca1-2ecc6da22a12","added_by":"auto","created_at":"2025-06-03 07:46:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39327,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7.1 Precision Confidence Curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/1b4d15034002e1bb73deb4c6.png"},{"id":83816097,"identity":"61b7c9fe-9581-4f1a-bd7b-18affc23cdfc","added_by":"auto","created_at":"2025-06-03 07:46:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30640,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7.2 Confusion Matrix\u003c/p\u003e","description":"","filename":"7.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/0e13d37517ad680e37aefb10.png"},{"id":83817237,"identity":"8e6f7687-68ef-4530-922d-1fd68c912cb4","added_by":"auto","created_at":"2025-06-03 07:54:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":705614,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7.3 Training the batch in YOLOv11n\u003c/p\u003e","description":"","filename":"7.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/0cd9dbfdeada5b8817dc6628.png"},{"id":83815116,"identity":"3eb3f53c-dd68-43c8-becd-5111a86b8d85","added_by":"auto","created_at":"2025-06-03 07:38:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":545738,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7.4 Training the batch in YOLOv11n\u003c/p\u003e","description":"","filename":"7.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/fd71714e28cffd193827a952.png"},{"id":83815114,"identity":"984e591a-57ce-447d-8fc9-9c923c83c58f","added_by":"auto","created_at":"2025-06-03 07:38:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":118248,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7.5 Overall Results using YOLOv11\u003c/p\u003e","description":"","filename":"7.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/ec15b0323d5c33dd3b6de66b.png"},{"id":85469698,"identity":"cd54a6d5-65ff-4c5e-8116-f5396b3099bf","added_by":"auto","created_at":"2025-06-26 09:02:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3387900,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6734052/v1/167caefb-1560-4529-a136-ec6379995352.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Optimized Deep Learning Framework for Early Detection and Prevention of Forest Fires Using Advanced Training Techniques","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForest fires, among the most destructive natural disasters, cause severe harm to environment, economy, and society. They can cause property damage and human casualties, as well as the loss of biodiversity and climate change due to high carbon dioxide emissions. Forest fires are anticipated to increase in frequency and intensity due to increasing global temperatures and increasingly unpredictable weather patterns. This increasing danger emphasizes how urgently efficient early detection technologies are needed to lessen the effects of catastrophic disasters. Forests, as essential elements of terrestrial ecosystems, provide a diverse array of forestry products and are pivotal in sustaining ecological equilibrium, delivering substantial benefits to economy and environment. Forest fires are substantial natural disasters that threaten the integrity of forest ecosystems, lead to the depletion of global forest resources, that cause injuries to individuals. Researching early forest fire identification is essential. Due to the abundance of oxygen in forests and the rapid occurrence of air convection, wildfires propagate rapidly. Worst natural disasters, forest fires, damage the environment, economy, and civilization. The initial method of detecting forest fires was manual examination, which had a low efficiency and large costs associated with both human and material resources. While there are benefits to using conventional techniques for identifying forest fires, such as satellite surveillance, infrared sensors, and ground patrols, there are also important drawbacks. Although satellite imaging offers wide coverage, there are considerable lags between image captures due to its poor temporal resolution. Although they are good at identifying heat signatures, infrared sensors need a lot of upkeep and might be hampered by cloud cover. Ground patrols may only cover a small region and need a lot of resources. These drawbacks emphasize the necessity of increasingly sophisticated, real-time detection systems. Thus, sensor-based detection quickly replaced manual exams. Sensor-based detection system excels in restricted areas. A variety of sensors, including temperature, humidity, smoke, gas, and integrated sensors, are used to detect forest fires. Its installation costs are very high, its detecting range is limited, and it has challenging power supply networking and communication issues. Furthermore, the sensors are unable to deliver crucial visual data that would enable firefighters to quickly assess the condition at the fire scene. Satellite remote sensing is impacted by cloud cover and the weather. Sensor-based detection replaced manual exams quickly. In tight spaces, the sensor-based detection system works well. Satellites fail to recognize forest fires in real time and lack temporal and spatial resolution. Compared to satellite photos, the camera performs better in real-time as a sensor. It could detect forest fires in remote forests when mounted on a drone. In contrast to non-imaging sensors, including smoke alarms, cameras provide firefighters with a more detailed visual representation of forest fire regions. Researchers are beginning to apply digital image processing technology for forest fire detection as a result of the advancements in computer vision. Forest fires are very destructive and unpredictable. There are clear drawbacks to the conventional approaches of computer vision detection, sensor-based detection, satellite remote sensing, and manual inspection. Characteristics of forest fires could be learned and adaptively obtained through DL algorithms. However, satellites are still unable to combine temporal and spatial resolution for identifying forest fires in real-time. The fire detection system is important in today's scenario to prevent fire in dense forest areas to protect wildlife animals and nature’s resources. The main difficulty in detecting forest fires is striking a balance between speed and precision. For prompt intervention to avoid false alarms that might put a burden on resources and lead to complacency, early and accurate detection is essential. Particularly in big, rural locations that require prompt answers, current systems frequently fall short. Promising solutions are provided by deep learning and computer vision technologies, especially object identification models like the YOLO family. Because YOLO models are rapid, accurate, that have been effectively applied to a range of real-time detection tasks, they are ideal for detecting forest fires. DL concepts along with an efficient object detection algorithm such as YOLO v11[26], the forest fire can be detected in an earlier stage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe study \"A Multimodal DL Approach for Real-Time Fire Detection in Aerial Imagery\" presents a multimodal strategy that improves the model's efficacy relative to conventional (convolutional neural network) CNN techniques. Multimodal architecture attained\u0026nbsp;true positive rate (TPR) of 100% and\u0026nbsp;false positive rate(FPR) of 0%, whereas optimal single-stream CNN produced TPR97% and an FPR0.02% [1]. \"YOLOv10: Real-Time End-to-End Object Detection\" illustrates that YOLOv10 attains exceptional performance and minimal latency relative to other advanced detectors, establishing its superiority. [2]. \"Image fire detection algorithms based on\u0026nbsp;convolutional neural network\" presents an innovative fire detection methodology based on CNN architectures, including Faster R-CNN, R-FCN, SSD, and YOLO V3, attaining an accuracy of 83% [3]. \"False positive decrement research for fire and smoke detection in surveillance cameras using spatial and temporal features based on deep learning\" offers a solution for reducing false positive detections through\u0026nbsp;novel smoke detection algorithm that integrates spatial and temporal features based on DL [5]. \"Saliency detection and deep learning-based wildfire identification in UAV imagery\" introduces an innovative saliency detection technique designed to swiftly identify and segment primary fire zones in aerial photos utilizing Fire-Net, a 15-layer self-learning model [6]. “\u003cem\u003eA deep learning-based object identification system for forest fire detection.”\u0026nbsp;\u003c/em\u003eproposes DL-based smoke detection system utilizing RetinaNet and Faster R-CNN, with optimal results attained through Faster R-CNN, identifying smoke columns with approximately 80% F1-score and 90% detection rate. System has been verified utilizing HPWREN dataset, with an average detection time of 6.3 minutes from initiation of fire[30].\"Video-based fire detection using deep learning models\" presents DL approach for fire detection utilizing video sequences, employing Faster R-CNN to distinguish between fire and non-fire areas based on spatial characteristics [7]. \"Analysis of machine learning methods for wildlife security monitoring with unmanned aerial vehicles\" assesses ML and DL methodologies including Haar, LBP cascades, Faster R-CNN, SSD, and YOLO, determining that YOLO is most effective algorithm for this task [4]. “EfficientDet: Scalable and Efficient Object Detection” EfficientDet represents a series of object detectors that emphasize efficiency through the application of a unique BiFPN for feature fusion and a compound scaling approach to uniformly adjust all network components. This results in superior accuracy with substantially fewer parameters and computations relative to previous detectors.[20].\"Advanced Forest Fire Detection Using YOLOv10: A Deep Learning Approach\" Outlines a real-time forest fire detection system utilizing YOLOv10 models—namely \"YOLOv10n, YOLOv10s, YOLOv10m, and YOLOv10b\"—to capitalize on advanced object detection capabilities, hence improving precision and velocity for prompt fire detection. Research employs publicly accessible forest fire image dataset for training and assessment, analyzing efficacy of YOLOv10 variants—\"YOLOv10n (nano), YOLOv10s (small), YOLOv10m (medium), and YOLOv10b (balanced)\"—in forest fire detection [27]. \"YOLOv11: An Overview of the Key Architectural Enhancements\" emphasizes enhancements in YOLOv11, that enhance accuracy and processing velocity while minimizing requisite number of parameters, making it appropriate for diverse applications ranging from edge computing to cloud-based analysis [26]. Analysis of several fire detection techniques, including YOLO, R-CNN, EfficientDet, R-FCN, SSD, and RetinaNet, indicates that YOLO is most effective object detection algorithm. In light of these findings, we propose the YOLOv11n framework for detecting forest fires.\u003c/p\u003e"},{"header":"3. Comparative Analysis of Object Detection Models for Fire Recognition","content":"\u003cp\u003eObject detection is fundamental to computer vision, allowing machines to recognize and highlight objects in images or videos. Over the years, several state-of-the-art algorithms have been developed, each with unique strengths and trade-offs. This section compares five prominent object detection models: YOLO[26], EfficientDet[20], RetinaNet[30], Faster R-CNN[4], and Mask R-CNN[31]. YOLO is distinguished for its real-time object identification skills, utilizing a singular CNN to directly predicted bounding boxes and class probabilities, resulting in remarkable speed. Recent versions, including YOLOv11, utilize advanced methodologies including transformers and attention mechanisms to enhance precision. EfficientDet balances speed and accuracy by leveraging EfficientNet as its backbone and employing BiFPN for feature fusion, achieving high accuracy with reduced computational complexity. RetinaNet reduces the disparity among foreground and background classes by a focal loss function, offering an effective balance between speed and accuracy, hence rendering it appropriate for\u0026nbsp;detection of small and densely packed objects. Dual-stage detector known as Faster R-CNN first generates region recommendations prior to classifying them. While computationally intensive, it delivers high accuracy, particularly for complex scenes with overlapping objects. An improvement on \u0026quot;Faster R-CNN, Mask R-CNN\u0026quot; has a branch for pixel-level segmentation masks, thereby making it appropriate for applications that require instance segmentation and object detection.\u003c/p\u003e\n\u003cp\u003eTable 3.1 Comparison of Deep Learning Algorithms\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlgorithm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInference Time (ms/frame)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Strengths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYOLOv11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eReal-time speed and high accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eEfficientDet-D7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eBalance of accuracy and computational efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eRetinaNet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eExcellent for small and densely packed objects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eFaster R-CNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eHigh accuracy in complex scenes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eMask R-CNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eInstance segmentation and detailed analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe performance of these algorithms varies significantly based on their design and intended applications. With an impressive 94% accuracy rate and an inference time of approximately 12 milliseconds per frame, YOLOv11 is appropriate for real-time applications consisting of autonomous vehicles and surveillance. EfficientDet-D7 achieves 92% accuracy with an inference time of 50 milliseconds per frame, effectively balancing accuracy and processing efficiency. RetinaNet attains 91% accuracy and an inference duration of 45 milliseconds per frame, demonstrating superior performance in contexts including small and densely clustered objects. Faster R-CNN achieves 93% accuracy but requires 150 milliseconds per frame, making it suitable for high-accuracy tasks in complex scenes. Mask R-CNN achieves 93% accuracy comparable to Faster R-CNN while enhancing its functionality to include instance segmentation, with an inference time of 200 milliseconds per frame, making it suitable for offline processing and applications requiring meticulous pixel-level analysis. Every algorithm has advantages, and specific requirements of application determine that model is appropriate. YOLO\u0026rsquo;s speed, EfficientDet\u0026rsquo;s balance, RetinaNet\u0026rsquo;s specialization, and the precision of Faster and Mask R-CNNs cater to diverse needs in object detection tasks. By comprehending these trade-offs, practitioners may determine the most appropriate model for their application.\u003c/p\u003e"},{"header":"4. Comparative Study of YOLO Versions for Forest Fire Detection","content":"\u003cp\u003eYOLO\u0026nbsp;has improved object-detecting performance with each release, addressing previous limitations. This section compares YOLO versions v1\u0026ndash;v11\u0026apos;s architectural improvements and forest fire detection performance with minimal\u0026nbsp;training epochs.\u003c/p\u003e\n\u003ch3\u003e4.1 Evolution of YOLO Versions\u003c/h3\u003e\n\u003cp\u003eYOLOv1 introduced a grid-based detection system, unifying the tasks of object localization and classification in a single CNN. Despite its innovative approach, it struggled with small object detection and produced higher false positive rates. Anchor boxes and multi-scale training improved YOLOv2\u0026apos;s ability to recognize objects of various sizes. YOLOv3 further refined detection accuracy with the introduction of Darknet-53 as its backbone and multi-scale predictions, enhancing feature representation across layers. Subsequent versions continued to build on these foundations. YOLOv4 integrated CSPNet and PANet to improve feature aggregation and spatial attention, resulting in higher accuracy. YOLOv5 prioritized model efficiency, achieving comparable performance with reduced computational overhead. Dynamic and resource-constrained contexts benefit from YOLOv6\u0026apos;s performance and false positive reduction enhancements. YOLOv7 emphasized real-time processing through extended efficient layer aggregation networks, allowing rapid detection without compromising accuracy. YOLOv8 implemented dynamic anchor assignments and improved model scaling, providing greater adaptability for complex datasets Further advancements included YOLOv9\u0026apos;s adaptive learning rate schedulers and its heightened precision in challenging scenarios. YOLOv10 introduced transformer-based layers, improving contextual understanding and detection in cluttered scenes. Finally, YOLOv11 incorporated advanced scaled transformers and attention mechanisms, achieving the highest accuracy and computational efficiency among all versions.\u003c/p\u003e\n\u003ch3\u003e4.2 Performance Metrics\u003c/h3\u003e\n\u003cp\u003eOver 20,000 Roboflow annotated forest fire images have been utilized for assessing YOLO versions. On\u0026nbsp;\u0026quot;training (70%), validation (20%), and testing (10%)\u0026quot; subsets of dataset, each model had been trained for 10 epochs on \u0026quot;Google Colab utilizing an NVIDIA Tesla T4 GPU\u0026quot;. Precision, recall, and mAP have been employed for evaluating each version. Results are in table below:\u003c/p\u003e\n\u003ch3\u003eTable 4.1 Yolo Version comparison\u003c/h3\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"512\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYOLO Version\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emAP (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining Time (Epochs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e72.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e77.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e86.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e93.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e92.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eYOLOv11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e4.3 Analysis and Implications\u003c/h3\u003e\n\u003cp\u003eMost effective model had been YOLOv11, that achieved 95%precision and 94%mAP with minimal training epochs. Advanced backbone networks and attention techniques enabled it detect forest fires in challenging conditions. These results demonstrate that architectural improvements improve detection accuracy and robustness. Results demonstrate that contemporary YOLO designs are suitable for detecting forest fires in real-time. Due to its streamlined processing and stable performance, YOLOv11 could be employed in crucial applications for early fire detection and mitigation. As forest fires become more prevalent and intense owing to climate change, integrating advanced AI-based systems into monitoring frameworks might improve response times and resource allocation, protecting ecosystems and humans.\u003c/p\u003e"},{"header":"5. Proposed Work","content":"\u003cp\u003eFire incident is a serious concern in forest areas which destroy natural resources human life and also wildlife. The proposed research work aims at detecting the fire/smoke at the earlier stage and alarms the forest officer to take remedial measures. The proposed framework aims at detecting the fire using YOLOv11 with modification. Joseph Redmon\u0026apos;s YOLO algorithm advances real-time object detection. Despite other approaches, YOLO considers object detection as a regression job that predicts bounding boxes and class probabilities from complete images. This novel method accelerates detection speed. The most recent version, YOLOv11, builds upon its predecessors by incorporating enhanced architecture and advanced training techniques. By adding few more changes to YOLOv11 framework more accuracy can be obtained. Figure 3 illustrates scenario of forest fires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 YOLO v11\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe YOLO 11 appears to be the best model AI for faster computations and comprehending images, even the smallest details with best resolutions. The latest YOLO version is offering up to 10% accuracy increase compared to previous iterations, something that may seem like a minor improvement but it\u0026rsquo;s enough for the majority of practical application cases.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003eTable 5.1 \u0026nbsp;Supported Tasks and Modes [32]\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe introduction of a single neural network architecture incorporating both bounding box regression and object classification within the YOLO framework has fundamentally altered the object detection paradigm [17]. The entire paradigm of approaches aimed at solving the problem of detection was sharply modified as the traditional two-stage methodologies were complemented by the possibility of end-to-end training of the network due to the fully differentiable architecture. Generally, the YOLO architecture is made up of three basic building blocks. The first and foremost is the backbone which is the basic feature extraction layer that applies CNNs to process input images into multiple feature maps at different scales. The second is the neck which is designed as a connection structure and functions as an intermediate processing element that uses combination layers to integrate feature representations at different scales and refine them. The third is the head which is the output block that serves the purpose of predicting the location and the class of objects apparent in an image using the refined feature maps as inputs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 YOLOv11 Algorithm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYOLOv11 is a real-time object detection system that improves on its predecessors. It employs an improved backbone architecture, such as CSPNet, for efficient feature extraction, and incorporates multi-scale detection through feature pyramid and path aggregation networks for handling objects of varying sizes. Attention mechanisms, like SE blocks or CBAM[24], are integrated to focus on critical regions, reducing false detections. YOLOv11 optimizes anchor box design using clustering algorithms tailored to specific datasets, enhancing bounding box accuracy. To improve computational efficiency, it utilizes depthwise separable convolutions, while a refined loss function balances classification, localization, and confidence scoring. Generalization is strengthened by advanced data augmentation methods including MixUp and Mosaic, resulting in it being resistant to a variety of circumstances. With real-time inference capabilities and high detection accuracy, YOLOv11 is particularly suited for time-critical tasks like forest fire detection, ensuring swift response and effective resource protection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 YOLO v11 Architecture\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith several architectural improvements intended to increase computing efficiency without compromising accuracy, YOLO11 launched in September 2024. This version introduces innovative components such as the C3k2 block and the C2PSA block, which facilitate better feature extraction and processing capabilities. Consequently, it achieves slightly improved performance with significantly fewer parameters in the model. The following are the key features of YOLO11. C3k2 Block: A less computationally demanding variant of Cross Stage Partial (CSP) Bottleneck, C3k2 block is a recent addition to YOLO11. It reduces processing time by replacing C2f block in neck and backbone with 2 smaller convolutions rather than a single large convolution. C2PSA Block: C2PSA block, which follows \u0026quot;Spatial Pyramid Pooling\u0026ndash;Fast (SPPF)\u0026quot; block, enhances spatial attention through \u0026quot;Cross Stage Partial with Spatial Attention mechanism\u0026quot;. Improved detection accuracy may result from model\u0026apos;s ability to concentrate more efficiently on significant areas of image due to this attention feature.\u003c/p\u003e\n\u003cp\u003eTable 5.2 Performance Metrics[33]\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"6. Experiment Analysis","content":"\u003cp\u003e\u003cstrong\u003e6.1 Datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe efficacy of any ML\u0026nbsp;model, especially in computer vision, is greatly influenced by\u0026nbsp;quality and diversity of datasets employed for training and assessment. For current research, we employed a publicly available dataset of images from forest fires from several online repositories. Images from various viewpoints and circumstances, including aerial and ground-level photographs, are included in the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2 Data Set Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected an extensive dataset from Roboflow for evaluating detection algorithm\u0026apos;s performance[28]. This dataset consists of 17,000 image samples demonstrating smoke and fire. It mostly includes images of forest fires, with a smaller number of images of urban fires that depict incidents of varying scales. This dataset\u0026apos;s diversity improves algorithm\u0026apos;s capacity in generalizing across various fire-related situations. Training, validation, and test sets have been produced from dataset.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3 Experimental Environment and Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor current research, we applied Google Colab[29] \u0026nbsp;in experiment with T4 GPU and coding using \u0026nbsp;Python. After performing the object detection using YOLOv11 the following results were obtained. YOLOv11 object detection mechanism detects the fire from the trained dataset. Real-time dataset collected from normal camera was also detected with higher accuracy. Due to overfitting the epoch was limited to 20. In YOLOv11 we chose the nano version. YOLOv11n (nano version) represents a compact and efficient iteration of YOLOv11, tailored for edge devices or scenarios that demand lower computational resources while still delivering exceptional performance in object detection tasks.YOLOv11n has been fine-tuned to possess fewer parameters compared to its larger counterparts (such as YOLOv11 or YOLOv11x). This optimization leads to decreased memory usage and faster inference times, making it particularly suitable for mobile devices, embedded systems, and real-time applications like drones and surveillance systems. Despite its reduced architecture, YOLOv11n can achieve processing speeds of up to 60 frames per second, which is essential for applications where timely responses are critical. Although it is smaller in size, YOLOv11n retains high level of accuracy for object detection, employing optimized feature extraction techniques to identify small and occluded objects, akin to its larger versions. The streamlined model size allows it to operate effectively on devices with limited processing capabilities, striking a favorable balance between speed and accuracy. Therefore, for those engaged with edge devices or in need of a lightweight model for real-time detection, YOLOv11n is an excellent option due to its rapid inference capabilities and reduced memory requirements, all while maintaining robust detection performance. Datasets perform crucial functions. In the proposed technique various datasets were used out of which, large dataset with minimum epochs gives higher accuracy compared to medium-sized dataset with higher epochs such as 50 or more. Thus we conclude that our paper using yolov11n shows higher accuracy similar to yolov11x which requires more computational resources, with the yolov11n version we can get higher accuracy with minimum resources like GPU and also increases the FPS speed since it is the nano version. Thus if we want to deploy model on edge devices mobile devices and drones, YOLOv11n can be used provided all the considerations are made.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4 \u0026nbsp; Predictive Model Evaluation\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Precision\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrecision measures proportion of expected positive cases that are\u0026nbsp;TP. High Precision: demonstrates that\u0026nbsp;majority of positive predictions are accurate, indicating a low rate of FP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026quot;\u003c/strong\u003eP = (TP) / (TP + FP)\u0026quot;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eRecall (Sensitivity or True Positive Rate)\u003c/h3\u003e\n\u003cp\u003eRecall quantifies percentage of TP cases that are accurately detected. A low percentage of FNs is demonstrated by a high recall, indicating that model is detecting majority of TP.\u003c/p\u003e\n\u003cp\u003eR = (TP)/(TP + FN)\u003cbr\u003e \u003cstrong\u003eAccuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;A=(TP+TN)/(TP+TN+FP+FN)\u0026quot;\u003cbr\u003e\u0026nbsp;Where:\u003cbr\u003e\u0026nbsp; A= Accuracy\u003cbr\u003e\u0026nbsp; TP = True Positives\u003cbr\u003e\u0026nbsp; TN = True Negatives\u003cbr\u003e\u0026nbsp; FP = False Positives\u003cbr\u003e\u0026nbsp; FN = False Negatives\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003e(True Positives)TP: Correctly predicted objects (i.e., class and bounding box are correct).\u003c/p\u003e\n\u003cp\u003e(False Positives)FP: Incorrectly predicted objects (i.e., the class is wrong or the bounding box doesn\u0026apos;t match).\u003c/p\u003e\n\u003cp\u003eTrue Negatives (TN): Correctly predicted negative instances (not often used in object detection tasks, as negative samples are typically not explicitly considered).\u003c/p\u003e\n\u003cp\u003eFalse Negatives (FN): Objects that were not detected but should have been.\u003c/p\u003e\n\u003ch4\u003eKey Steps in the Implementation\u003c/h4\u003e\n\u003cp\u003e1. \u003cstrong\u003eEnvironment Setup\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ei. \u0026nbsp; \u0026nbsp;Required libraries, including ultralytics for YOLO and roboflow, are installed.\u003c/p\u003e\n\u003cp\u003eii. \u0026nbsp; Roboflow is used to manage and download the dataset.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eDataset Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ei. \u0026nbsp; \u0026nbsp;The dataset is accessed via Roboflow using an API key.\u003c/p\u003e\n\u003cp\u003eii. \u0026nbsp; The FireMix project from the workspace is selected, and version 9 of the dataset is downloaded in YOLOv11 format.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eModel Initialization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ei. \u0026nbsp; \u0026nbsp;A pre-trained YOLOv11 model is loaded. Various configurations of the model (e.g., n, s, m, l, x) can be utilized depending on the specific requirements.\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eModel Training\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ei. \u0026nbsp; \u0026nbsp;data.yaml file contains custom dataset that is employed to train YOLOv11 model.\u003c/p\u003e\n\u003cp\u003eii. \u0026nbsp; 640x640 pixel input image has been employed for training over 20 epochs.\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eOutput\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ei. \u0026nbsp; \u0026nbsp;After training, model is expected to produce a robust system capable of identifying forest fires from images.\u003c/p\u003e"},{"header":"7. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Precision Confidence Curve\u003c/h2\u003e \u003cp\u003eA \u003cb\u003ePrecision Confidence Curve\u003c/b\u003e is a visualization used to evaluate a model's performance by showing how confident predictions relate to precision. It provides insights into the confidence threshold's impact on precision and helps fine-tune the threshold for specific applications. Model's performance for Fireresistances class is thoroughly examined employing Precision-Recall (PR) curve displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7.1\u003c/span\u003e. This graph illustrates that recall (sensitivity) and precision (positive predictive value) get traded off at different classification thresholds. At an Intersection over Union (IoU) threshold of 0.5, the model's mAP is 0.939. When PR is balanced, this statistic demonstrates a high degree of accuracy in identifying the target class. With few FP at higher thresholds, model appears to be performing extraordinarily well for high-confidence predictions, as indicated by a significant decrease in precision near end of curve. The close alignment between the Fireresistances class-specific curve and the overall mAP curve reflects the consistency and robustness of the model in identifying this particular class across the dataset. Applications requiring accurate categorization of fire-resistance data depend on the model's ability to manage complicated scenarios, that is demonstrated by its high mAP score.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Confusion Matrix\u003c/h2\u003e \u003cp\u003eA device for evaluating a model's performance is confusion matrix. It provides a thorough analysis of the model's performance, enabling users in computing crucial parameters including recall and precision.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e7.2.1 Confusion Matrix Analysis\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eConfusion matrix for model's classification performance on Fireresistances class appears in\u003c/span\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7.2\u003c/span\u003e. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe confusion matrix provides a detailed breakdown of the model\u0026rsquo;s predictions, highlighting the distribution of TP, FP, TN, and FN. TP (3704): 3704 instances of the Fire resistance class had been accurately identified by model. FP (672): 672 occurrences of the background class had been misclassified as Fireresistances by model.TN (N/A): If total counts have been determined, it could infer number of correctly detected background occurrences, even though it isn't explicitly displayed. FN (255): model misclassified 255 instances of the Fireresistances class as background since it failed to detect them. With a high number of TP and a comparatively low FN, confusion matrix depicts that model performs well in classification. FP, while higher in count, are within acceptable limits, indicating a moderate level of misclassification between background and Fireresistances. Matrix supports model's potential for real-world application in situations requiring precise fire-resistance classification by graphically highlighting the degree to which it separates the two groups.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Training the batch in YOLOv11\u003c/h2\u003e \u003cp\u003eFigure\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7.3\u003c/span\u003e and 7.4 illustrate detection results of the fire classification model applied to diverse set of test images. Each image demonstrates model's ability in localizing fire regions effectively across varying contexts, including urban fires, forest fires, and controlled flames.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBounding Boxes\u003c/b\u003e: The blue bounding boxes indicate regions identified as fire by the model. Each box corresponds to a detected fire instance with high confidence.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiversity in Contexts\u003c/b\u003e: The model exhibits strong detection skills in a variety of scales and lighting situations, in addition to a range of environmental variables, including open fields, dense forests, and urban settings.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Overall results\u003c/h2\u003e \u003cp\u003eFigure below displays overall YOLO v11n object detection model results on dataset.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e7.4.1 Interpretation Based on the Graphs:\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTraining Loss Metrics\u003c/b\u003e:\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 7.1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTraining Loss Metrics \u003cb\u003eValidation Loss Metrics\u003c/b\u003e:\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproximate Starting Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApproximate Ending Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etrain/box_loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe decreasing trend indicates the model is improving at localizing object bounding boxes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etrain/cls_loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe classification error is reducing, meaning the model is better at differentiating object classes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etrain/dfl_loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe model's confidence in accurate bounding box predictions is improving.\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7.2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValidation Loss Metrics: \u003cb\u003ePerformance Metrics\u003c/b\u003e:\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproximate Starting Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApproximate Ending Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eval/box_loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe decreasing validation box loss suggests the model is generalizing well.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eval/cls_loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClassification loss is improving, indicating better validation accuracy.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eval/dfl_loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA steady decrease reflects improved performance on unseen data.\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7.3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance Metrics\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproximate Starting Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApproximate Ending Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetrics/precision(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrecision is increasing, meaning fewer FP.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetrics/recall(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher recall indicates better object detection coverage.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetrics/mAP50(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel's 50% IoU detection performance is improving.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetrics/mAP50-95(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel performance is strong when IoU thresholds increase.\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\u003eThe analysis of the training and validation performance metrics reveals several key observations and research insights. Initially, the model demonstrates learning without overfitting, as evidenced by the steady decline in training and validation losses, including \"box loss, classification loss, and distribution focal loss\". Model appears to be well-regularized, ensuring strong generalization to unknown data, as evidenced by the slight difference among training and validation losses. Second, model's improved capacity to precisely recognize objects while minimizing FP is demonstrated by performance metrics including precision and recall, indicating a consistent improvement. The model's improving capacity for determining objects with more precision and consistency is further supported by increasing trend in mAP metrics across different IoU thresholds. Lastly, the presence of smooth trend lines, represented by the orange dotted lines in the plots, indicates that the model's learning process is stable across epochs without erratic fluctuations, signifying a well-converged training process. Collectively, these observations demonstrate reliability and efficiency of model at object-detecting tasks.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThe current research concludes by highlighting the efficient application of YOLO v11n model for forest fire detection, proving its capacity to provide prompt and precise fire identification. The research utilized Google Colab with T4 GPU for coding in Python, which facilitated robust experimentation. The YOLO v11 object detection mechanism successfully identified fire from a trained dataset, and real-time data collected from standard cameras exhibited high accuracy levels. For reducing overfitting, training has been limited to 20epochs, ensuring optimal performance. YOLOv11n nano version has been selected since it is small and efficient for edge devices and lower computational resource applications. YOLOv11n can manage 60 frames per second, therefore being appropriate for real-time applications including drones and surveillance systems despite its reduced architecture. The enhanced feature extraction approaches of this model could recognize small and obstructed objects in addition to larger models while using less memory. The study also emphasizes the critical role of dataset selection, revealing that using a large dataset with fewer epochs yields higher accuracy compared to smaller datasets with more epochs. Consequently, the YOLOv11n version achieves impressive accuracy comparable to the larger YOLOv11x model, but with far lower resource requirements. It balances speed and accuracy, thus making it ideal for edge devices, mobile platforms, and drones. Overall, this research underscores the importance of innovative technologies in forest fire detection and highlights the potential for collaboration with environmental scientists and fire management.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePosch, J. R., Daniel, A. (2019). A multimodal deep learning approach for real-time fire detection in aerial imagery. \u003cem\u003eChalmers University Of Technology\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. \u003cem\u003eTsinghua University\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLi, P., Zhao, W. (2020). Image fire detection algorithms based on convolutional neural networks. \u003cem\u003eScience Direct Elsevier, Volume 19, June 2020\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAlexandrov, D., Pertseva, E., Berman, I., Pantiukhin, I., Kapitonov, A. (n.d.). Analysis of Machine Learning Methods for Wildfire Security Monitoring with Unmanned Aerial Vehicles. \u003cem\u003eProceeding of The 24th Conference of Fruct Association\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLee, Y., Shim, J. (2019). False Positive Decremented Research for Fire and Smoke Detection in Surveillance Camera using Spatial and Temporal Features Based on Deep Learning. \u003cem\u003eElectronics, 15 October 2019\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eZhao, Y., Ma, J., Li, X., Zhang, J. (2018). Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery. \u003cem\u003eSensors, 27 February 2018\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eKim, B., Lee, J. (2019). A Video-Based Fire Detection Using Deep Learning Models. \u003cem\u003eApplied Science, 18 July 2019\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWu, L., Chen, L., Hao, X. (2021). Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network. \u003cem\u003eInformation 2021, 12, 59\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eSol\u0026oacute;rzano, A., Eichmann, J., Fern\u0026aacute;ndez, L., Ziems, B., Jim\u0026eacute;nez-Soto, J. M., Marco, S., Fonollosa, J. (2022). Early fire detection based on gas sensor arrays: Multivariate calibration and validation. \u003cem\u003eSens. Actuators B Chem., 352, 130961\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eZheng, S., Gao, P., Wang, W., Zou, X. (2022). A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network. \u003cem\u003eAppl. Sci. 2022, 12, 6721\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRui, X., Li, Z., Zhang, X., Li, Z., Song, W. (n.d.). A RGB-Thermal based adaptive modality learning network for day\u0026ndash;night wildfire identification.\u003c/li\u003e\n\u003cli\u003eBouguettaya, A., Zarzour, H., Taberkit, A. M., Kechida, A. (2022). A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. \u003cem\u003eSignal Process\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eLi, Y., Zhang, W., Liu, Y., Jing, R., Liu, C. (2022). An efficient fire and smoke detection algorithm based on an end-to-end structured network. \u003cem\u003eEng. Appl. Artif. Intell., 116, 105492\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eCarion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. \u003cem\u003eComput. Vis. ECCV 2020, 2020, 213\u0026ndash;229\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eQin, Y. Y., Cao, J. T., Ji, X. F. (2021). Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3. \u003cem\u003eInt. J. Autom. Comput., 18, 300\u0026ndash;310\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRedmon, J., Farhadi, A. (2018). Yolov3: An Incremental Improvement. \u003cem\u003earXiv 2018, arXiv:1804.02767\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWang, Y., Hua, C., Ding, W., Wu, R. (2022). Real-time detection of flame and smoke using an improved YOLOv4 network. \u003cem\u003eSignal Image Video Process., 16, 1109\u0026ndash;1116\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eHoward, A., Sandler, M., Chu, G., Chen, L. C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al. (2019). Searching for MobileNetV3. \u003cem\u003eProceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October\u0026ndash;2 November 2019, pp. 1314\u0026ndash;1324\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBochkovskiy, A., Wang, C. Y., Liao, H. Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. \u003cem\u003earXiv 2020, arXiv:2004.10934\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eTan, M., Pang, R., Le, Q. V. (2020). EfficientDet: Scalable and Efficient Object Detection. \u003cem\u003eProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14\u0026ndash;19 June 2020, pp. 10781\u0026ndash;10790\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBahhar, C., Ksibi, A., Ayadi, M., Jamjoom, M. M., Ullah, Z., Soufiene, B. O., Sakli, H. (2023). Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN. \u003cem\u003eElectronics 2023, 12, 228\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eYu, S., Sun, C., Wang, X., Li, B. (2022). Forest fire detection algorithm based on Improved YOLOv5. \u003cem\u003eJ. Phys. Conf. Ser., 2384, 012046\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDou, Z., Zhou, H., Liu, Z., Hu, Y., Wang, P., Zhang, J., Wang, Q., Chen, L., Diao, X., Li, J. (2023). An Improved YOLOv5s Fire Detection Model. \u003cem\u003eFire Technol.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eWoo, S., Park, J., Lee, J. Y., Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module. \u003cem\u003eProceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8\u0026ndash;14 September 2018, pp. 3\u0026ndash;19\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDu, J. (2018). Understanding of object detection based on cnn family and yolo. \u003cem\u003eJournal of Physics: Conference Series, volume 1004, page 012029. IOP Publishing\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eKhanam, R., Hussain, M. (2024). Yolov11: An Overview Of The Key Architectural Enhancements. \u003cem\u003earXiv:2410.17725, October 2024\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003ePatel, S. H., Patel, D. H., Bankar, N. M., Chaudhari, N. K., Dave, U. R., Prajapati, N. K. (2024). Advanced Forest Fire Detection Using YOLOv10: A Deep Learning Approach. \u003cem\u003eJ. Electrical Systems, 20-3, 4115-4125\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRoboflow. Retrieved from https://universe.roboflow.com/.\u003c/li\u003e\n\u003cli\u003eGoogle Colab. Retrieved from https://colab.research.google.com/.\u003c/li\u003e\n\u003cli\u003eGuede-Fern\u0026aacute;ndez, F., Martins, L., de Almeida, R. V., Gamboa, H., \u0026amp; Vieira, P. (2021). \u003cem\u003eA deep learning-based object identification system for forest fire detection.\u003c/em\u003e Fire, 4(4), 75.\u003c/li\u003e\n\u003cli\u003eJahagirdar, A., Sathe, N., Thorat, S., Saxena, S. (n.d.). Fire detection in nano-satellite imagery using Mask R-CNN. \u003cem\u003eInternational Journal of Signal and Imaging Systems Engineering\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003ehttps://docs.ultralytics.com/models/yolo11/#supported-tasks-and-modes\u003c/li\u003e\n\u003cli\u003ehttps://docs.ultralytics.com/models/yolo11/#performance-metrics \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"forest fire, YOLO, YOLOv11n, Convolutional Neural Networks., Dataset, epoch","lastPublishedDoi":"10.21203/rs.3.rs-6734052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6734052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eForests are indispensable ecosystems that sustain biodiversity, regulate the global climate, and provide essential resources for life on Earth. However, these critical habitats are increasingly jeopardized by forest fires, a major environmental challenge that leads to severe ecological, economic, and social repercussions. Forest fires contribute to the destruction of flora and fauna, exacerbate greenhouse gas emissions, and disrupt the ecological equilibrium. To mitigate such disasters, early detection systems are crucial for enabling rapid response, minimizing damage, and preserving these vital ecosystems. This research presents a novel forest fire detection system utilizing YOLO v11 (You Only Look Once) deep learning model. YOLO, recognized for its real-time object identification proficiency, is further refined in this version and addresses the specific issues associated with detecting forest fires. The suggested technique emphasizes early detection by precisely identifying fire patterns in images or videos, regardless of adverse environmental conditions that include inadequate lighting, dense smoke, or obstructions. The methodology incorporates advanced deep learning techniques, optimized network architectures, and a comprehensive dataset comprising diverse scenarios of forest fire outbreaks. By training YOLO v11 model on annotated datasets, the system achieves high precision and recall rates, ensuring minimal false positives and negatives. This approach enables rapid identification and localization of fire hotspots, facilitating immediate intervention to contain and extinguish fires. Integration of this model into forest fire management systems provides a robust tool for real-time monitoring and decision-making. It can be deployed through aerial surveillance using drones, fixed camera installations, or satellite imagery analysis, offering scalability and adaptability for different environments. Additionally, the system supports predictive modeling to analyze fire spread patterns, enhancing proactive measures for disaster prevention. Implementation of this advanced detection system represents a significant leap in forest conservation and disaster management efforts. It underscores the potential of artificial intelligence and deep learning(DL) in addressing global environmental challenges, safeguarding biodiversity, and promoting sustainable development. This project ensures ecological and economic stability in the face of climate change by reducing forest fire damage.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"An Optimized Deep Learning Framework for Early Detection and Prevention of Forest Fires Using Advanced Training Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 07:37:55","doi":"10.21203/rs.3.rs-6734052/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d40599ca-8048-499e-86b3-7acf8637899c","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-26T08:54:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 07:37:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6734052","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6734052","identity":"rs-6734052","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}