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In the defect detection of hot-rolled strip steel, there are often problems of too small target size and unclear features that lead to wrong detection and missed detection, for which a YOLOv5-based defect detection method for hot-rolled strip steel is proposed in this paper. Firstly, the overall architecture of the method is proposed, and then the algorithm implementation process is highlighted. Experimental analysis shows that the average detection accuracy using YOLOv5 is improved by 11.9% compared to YOLOv4 improved, with stronger generalization capability, faster detection speed, and lower error and miss detection rate.

YOLOv5-based Defect Detection Model for Hot Rolled Strip Steel