The paper proposes a YOLOPCB network for small defects detection on printed circuit board(PCB)using multi-channel feature fusion learning.Firstly,the last group of MPConv layer and E-ELAN layer in the YOLOv7 backbone network are removed,and the ECU module in the fusion layer and the 20×20 prediction head are eliminated.A cross-channel information connection module(CIC)is utilized to link the streamlined backbone and fusion networks.Secondly,a shallow feature fusion module(SFF)and a new anchor matching strategy are designed,which add two low-level,high-resolution detection heads.Lastly,the three E-ELAN layers in the YOLOv7 backbone network are used as inputs,while the bottommost E-ELAN and two concatenation modules in the fusion layer are used as outputs,with adaptive weighted skip-connection(AWS)to increase the information within the same dimension.The average precision on the PCB Defect datasets reaches 94.9%,with a detection speed of 45.6 fps.Furthermore,on the Self-PCB datasets obtained from on-site enterprises,YOLOPCB achieves the highest accuracy of 76.7%,which is a 6.8%improvement over the detection accuracy of YOLOv7.YOLOPCB effectively enhances the detection capability of small defects on printed circuit boards.
维普
万方数据