Improved Steel Surface Defect Detection Algorithm Based on YOLOv5s Network
Aiming at the problems of low detection accuracy,slow detection speed and high model complexity of the current steel surface defect detection algorithm,an improved steel surface defect detection algorithm based on YOLOv5s was proposed.The SE chan-nel attention module was integrated into the backbone network to increase the weight of defect feature channels,reduce background in-terference,and improve the extraction ability of the algorithm for defect features.The STR multi-head self-attention module was inte-grated into the neck network to increase the proportion of detail features such as defect edge texture.The loss function was improved to SIoU to shorten the prediction box regression convergence process and improve the algorithm detection speed.The experimental results show that the mAP value of the improved algorithm on the NEU-DET dataset is 80.4％,which is 5.5％higher than that of YOLOv5s,the number of processed frames per second is 100,the algorithm volume is reduced by about 8.3％,and the algorithm calculation amount is reduced by about 4.3％.Compared with other target detection algorithms,the improved algorithm has significantly improved the detection accuracy and detection speed,and the complexity of the model is significantly reduced.The improved algorithms meet the needs of real-time steel surface defect detection.
万方数据
维普
