Defect Detection Algorithm of Bearing End Face Based on Improved YOLO v3
In order to improve the detection speed and accuracy of bearing end face defects,a defect detection algorithm of bearing end face based on improved YOLO v3 was proposed.The image data set was enhanced to prevent overfitting phenomenon.The improved K-means clustering algorithm was used to re-cluster Anchor Boxes for target detection,and SKNet attention mechanism module was in-troduced to improve the original network structure and output layer structure.Finally,the improved YOLO v3 algorithm was verified by experiment and compared with the original YOLO v3 algorithm.The results show that the mAP value of the improved YOLO v3 algorithm for the detection of bearing end face defects is increased by 7.03％and the detection speed is increased by 34.7 fps,which verifies the effectiveness of the improved algorithm.
万方数据
维普
