Improved YOLOV5 based industrial surface defect detection method

Zefeng Li, Zeyu Zhang, Jixin Ma, Lele Tian

Abstract: in industrial production, defects on the surface of a product can affect the quality and performance of the product, which in turn affects the product’s competitiveness in the market. Therefore, effective surface defect detection is very important to ensure product quality, improve production efficiency and reduce production costs. The use of computer-vision surface defect detection technology in the production process of industrial manufacturing enterprises is able to not only improve product quality and market competitiveness, but also realize production automation and intelligence and promote the development of enterprises towards a more efficient and intelligent direction. In order to improve the small target defect detection capability of the defect detection algorithm model and solve the problem of poor defect location, an improved YOLOV5 algorithm is proposed in this article. The K-Means++ algorithm is used instead of the K-Means algorithm to determine the size of the candidate frame and the ARM, BFF, and ODM modules are added to filter out some samples with small intersection areas of the real candidate frame, and the specific candidate frame size is optimized automatically. Finally, in order to improve the global feature extraction capability and small target detection capability of the algorithm model, the tail of the backbone network in YOLOV5 is changed to the Transformer coder model. The tested average accuracy of the modified algorithm is 70.3%, which is higher than the experimental 65.6% before the modification and achieves good results in defect detection.