Road Defect Detection Based on Yolov5 Algorithm

In response to the time-consuming and inconvenient drawbacks of traditional manual detection methods for pavement defect detection, coupled with the rapid development of deep learning in recent years, the paper proposes a defect recognition method based on Yolov5. Yolov5 is a very good deep learning algorithm for target detection and defect recognition. In terms of advantages, it adopts a more advanced network structure with some new techniques such as adaptive convolution, SPP structure and PAN structure. The Yolov5 network is first trained on a manually annotated dataset, and then we use the trained model to perform defect recognition on the images to be detected.