Abstract: Aiming at the problems of large amount of calculation,large amount of parameters,slow detection speed and low detection accuracy of existing defect detection algorithms,this paper proposes a high-precision and real-time detection algorithm for photovoltaic glass edge defects based on YOLOv5.Firstly,a newly designed dense connection block(New_DBlock(C)) based on cheap operation and feature reuse is used to replace the C3 block of YOLOv5’s feature extraction network,which reduced the calculation amount and parameter amount of the whole algorithm.Secondly,the C2f_SE block fused with the channel attention mechanism SE(Squeeze-and-Excitation) is used to replace the C3 block of the YOLOv5’s feature fusion network to improve the detection speed and detection accuracy.Finally,the improved YOLOv8’s decoupling detection head is used to replace the coupling detection head of YOLOv5 to improve the positioning accuracy and classification accuracy of the algorithm.The experimental results show that the improved algorithm mAP@0.5 is increased by 1.0%,mAP@0.5:0.95 is increased by 3.1%,the amount of calculation is decreased by 48.1%,the amount of parameters is decreased by 56.7%,and the detection speed is increased by 18.5%.Compared with other mainstream YOLO and R-CNN series algorithms,the improved algorithm also has higher detection accuracy,detection speed,and lower amount of calculation and parameters,which is suitable for the real-time detection of photovoltaic glass edge defects.

Key words: Photovoltaic glass, Defect detection, YOLOv5s, Object detection, Attention mechanism