Abstract: Aiming at the problems of complex structure,large number of parameters,poor detection accuracy and real-time performance of existing steel surface defect detection models,this paper proposes an improved YOLOv5s lightweight steel surface defect detection model.Firstly,the MobileNetv3-Small is used to replace the YOLOv5s backbone extraction network,achieving model lightweight and improving detection speed.Secondly,in the feature fusion stage,a weighted bidirectional feature pyramid network (BiFPN) is used to enhance feature extraction.By fusing features of different scales,the accuracy and robustness of detection are improved.Simultaneously,the convolutional block attention module(CBAM) attention mechanism is introduced to enhance the model's ability to detect small scale targets.Finally,the K-means++ algorithm is proposed to cluster prior boxes,improve the accuracy and convergence speed of prior box clustering.The average accuracy of the improved YOLOv5s on the NEU-DET dataset (mAP@0.5) reaches 77.2%,with a detection speed of 102 FPS on NVIDIA 1080Ti.Compared to the original YOLOv5s,the mAP is increased by 3.90%,the parameter quantity is decreased by 58.6%,the volume is decreased by 34%,and the detection speed is increased by 29.7%.Experimental results demonstrate that the improved lightweight YOLOv5s effectively improves both the accuracy and speed of steel surface defect detection.Moreover,it is easy to deploy and meet the requirements of actual production in the steel strip industry.

Key words: Defect detection, YOLOv5s, lightweight, MobileNetv3-Small, BiFPN, CBAM, K-means++