Research on strip surface defect detection based on improved YOLOv5 algorithm

In the production process of hot rolled strip, there will inevitably be defects such as iron scale, slag inclusion and scratch on the surface, and there is no effective identification method for these defects. Therefore, based on the improved YOLOv5s algorithm, this article proposes an improved YOLOv5s-GCB model by introducing the coordinate attention module, improving the feature fusion structure and lightening the feature extraction backbone network. The experiment shows that the precision of YOLOv5s-GCB is 89.7%, recall is 90.2% and mAP@0.5 is 93.1%, which are 2.1%, 3.0% and 1.6% higher than YOLOv5s, respectively. The average detection time of a single image is 7.9 ms, and the detection speed is improved. Finally, the ablation experiment shows that the proposed improved strategy is feasible, and the detection effect of YOLOv5s-GCB is further verified by the comparison experiment with other target detection models. Therefore, it can be concluded that the proposed method provides an important reference for the detection of indicated defects in strip steel.