Surface Defect Detection Algorithm of Shaft Parts Based on Improved YOLOv5
In view of the low detection accuracy caused by various types of surface defects of shaft parts and complex defect forms,an improved YOLOv5 surface defect detection method for target shaft parts was pro-posed in this paper.In order to solve the problem of often missing or failing to detect small target defects on the surface of shaft parts in daily production,a new small target detection layer has been added on the basis of the original YOLOv5,combining shallow feature maps with deep feature maps,making the entire network pay more attention to small target defects.At the same time,in order to solve multi-object defects and low detec-tion accuracy and missing detection of incomplete shaft parts,the shaft image data of multi-object defects and occlusion processing is added to the self-built data set in this paper.According to the comparison experi-ments,the detection performance of the improved YOLOV5 model is better than that of FasterRCNN,SSD and original YOLOV5,and the average accuracy of the test is higher than 7%,9%and 4%respectively.The conclusion proves that this method has higher accuracy in detecting surface defects of shaft components,and significantly improves the detection effect on multi-objective defects and incomplete shaft components.
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