Improved YOLOv5s-based surface defect detection method for steel

Steel is an important metal material, however, various surface defects, such as cracks, bubbles, inclusions, scratches, etc., it is bound to show up in the technical production of steel products. Therefore, the detection of surface defects of steel products is very important. However, defects with blurred edges and divergent shapes such as cracking and oxidation are usually difficult to locate precisely. Therefore, this paper combines the GAM attention mechanism with linear structural unit MV2Block to construct a steel surface defect detection model, aiming to improve the generally low detection accuracy of small defects. Aiming at the problems of fuzzy edges, divergent shapes and tiny defects in traditional defect detection, such as low precision or even undetectable, this paper proposes an intelligent defect detection model, YOLOv5s-MG, to solve the problem that defects are difficult to locate accurately in the detection process. Taking YOLOv5s as the benchmark model, this paper improves the model from three aspects: First, to solve the problem of ignoring global information in the model, a Generalized Additive Models (GAM) global attention mechanism is introduced into the defect detection model. Secondly, in order to solve the problem of nonlinear information loss, the MV2Block module is constructed. Finally, this paper cross-references GAM attention mechanism and MV2Block, and conducts experiments on nue-det dataset. It is found that the YOLOv5s-MG model proposed in this paper has the best effect on the improvement of intelligent inspection defect detection, and mAP has increased by 4.7% compared with the original model.