Aluminum Surface Defect Detection Algorithm Based on Improved YOLOv5

Abstract

Abstract Industrial defect detection is an important aspect of object detection. Aluminum is an indispensable material in the industrial field, but the complexity of surface defects on aluminum makes detection challenging. Therefore, the paper proposes the YOLOv5‐ESP algorithm based on the YOLOv5 algorithm. First, the problem of poor signal quality and small data samples is addressed through data enhancement. Second, the YOLOv5‐ESP incorporates the Efficient Channel Attention‐C3 (ECA‐C3) module in the Backbone structure to enhance attention toward defect regions. The Spatial Pooling Pyramid Cross‐Stage Partial Convolution (SPPCSPC) module is introduced to extract multi‐scale features of defects. The Poly‐scale Convolution (PSConv) is applied at the end of the Neck structure to resolve the problem of imprecise localization resulting from significant differences in the scale of defect features. Soft Non‐Maximum Suppression (Soft‐NMS) is utilized in the Head structure to optimize the prediction boxes. Experimental results show that the proposed YOLOv5‐ESP achieves a mean Average Precision (mAP) value of 94%, outperforming YOLOv5 and other classical algorithms. Furthermore, it maintains an average recognition time of 0.019 s per image, which meets the requirements of accuracy and real‐time performance in the industry.