Application Study of YOLOv5 Algorithm on Automotive Wheel Surface Defect Detection

Surface defect detection is an important part in the process of automotive wheel production. However, it is a challenging task because of complex background and a wide range of defect types. In order to detect the defects on the wheel surface accurately and quickly, this paper proposes a YOLOv5-based algorithm for automotive wheel surface defect detection. The algorithm uses YOLOv5s model to train and test on the self-built automotive wheel surface defect dataset which contains four kinds of defects, namely linear, dotted, sludge, pinhole. The extensive experimental results show that the deep learning network trained by our method can achieve an average accuracy of 71.4% and 33.66 FPS, reflecting that the detection effect is better than other common target detection algorithms, and meets the real-time requirements of industrial applications.