Research on Defect Detection Method of Stamping Parts Based on Improved YOLOv5
Stamped parts are prone to cracks, scratches, wrinkles, bumps and other defects in the production process. At present, the defect detection of stamped parts on the production line is based on manual detection, which is inefficient and prone to leakage. For this reason, a defect detection algorithm based on the improved YOLOv5 model is proposed. In order to improve the attention of the defective part and better focus the defects, this paper introduces the CA attention module in the backbone network of the YOLOv5 model. To further improve the accuracy of the model, this paper improves the localization accuracy by changing the target frame loss function to GIoU through comparative experiments. The experiments show that compared with the original model, the improved YOLOv5 model precision, recall, and mAP value are all improved.
万方数据
维普
