Study on Surface Defect Detection of Strip Steel Based on Machine Identification
In order to solve the problems of backward traditional strip surface defect detection technology,low efficiency and insuffi-cient small target identification ability,an improved YOLOv5s-Tiny target detection model was proposed,which improved the detection speed and recognition accuracy while maintaining the small calculation amount of the model.The backbone network GSP-Darknet53 was replaced with the lightweight GhostNet network to reduce the number of model parameters and improve the reasoning speed.The CBAM attention mechanism was added to the backbone network to enhance the feature information through channel attention mechanism and spatial attention mechanism to improve the detection accuracy of small target,and the loss function GIoU was improved to EIoU to im-prove the positioning ability of the detection box.Finally,the improved training model format was converted and installed to the Android terminal.The results show that in the Northeastern University data set,the mAP of the improved model is increased by 1.5％,the param-eter volume is reduced by 12.3％,the recall rate is increased by 1.5％,and the Android end detection speed is about 120 ms,which completes the real-time detection of strip steel defects.
万方数据
维普
