Lightweight Pipeline Defect Detection Algorithm Based on FALW-YOLOv8

Pipelines play a critical role in industrial production and daily life as essential conduits for transportation. However, defects frequently arise because of environmental and manufacturing factors, posing potential safety hazards. To address the limitations of traditional object detection methods, such as inefficient feature extraction and loss of critical information, this paper proposes an improved algorithm named FALW-YOLOv8, based on YOLOv8. The FasterBlock is integrated into the C2f module to replace standard convolutional layers, thereby reducing redundant computations and significantly enhancing the efficiency of feature extraction. Additionally, the ADown module is employed to improve multi-scale feature retention, while the LSKA attention mechanism is incorporated to optimize detection accuracy, particularly for small defects. The Wise-IoU v2 loss function is adopted to refine bounding box precision for complex samples. Experimental results demonstrate that the proposed FALW-YOLOv8 achieves a 5.8% improvement in mAP50, alongside a 34.8% reduction in parameters and a 30.86% decrease in computational cost. This approach effectively balances accuracy and efficiency, making it suitable for real-time industrial inspection applications.