Research on Pothole Road Detection and Recognition Based on PyTorch and Improved YOLOv5

Potholes on road surfaces are one of the pressing issues in urban road maintenance. They not only damage the stability and efficiency of vehicles but also pose a potential threat to the safety of pedestrians and passengers. In view of the limitations of YOLOv5 in feature representation, adaptability to complex scenes, and utilization efficiency of computational resources, this study introduces the CBAM (Convolutional Block Attention Module) attention mechanism into its backbone network. By combining channel attention and spatial attention, the model is able to focus more accurately on the key regions in the image, thereby optimizing feature representation. The improved network model is implemented using the deep learning framework PyTorch, and the model is quantitatively evaluated using metrics such as mAP (mean Average Precision). Experimental results demonstrate that the proposed model exhibits excellent performance in terms of detection accuracy and efficiency, which is of significant importance for research and applications in fields such as geological exploration, space science, and natural disasters.