Research on PCB Surface Defect Detection Based on Improved Faster RCNN
Printed circuit board(PCB)inevitably have tiny defects such as solder joints missing solder,shorts,burrs,nicks,open circuits,residual copper and so on during the manufacturing process.The traditional defect detection methods based on machine vision inspection have problems such as slow detection speed,high false detection and leakage rates,and weak anti-interference ability and so on.To solve the above problems,a PCB surface defect detection method based on improved faster region convolutional neural network(Faster RCNN)is proposed.Firstly,on the basis of the traditional Faster RCNN framework,an extended feature pyramid network(EFPN)is incorporated to achieve feature extraction and fusion for multi-scale detection to maximize the retention of image detail information to improve the detection performance.Secondly,the K-means algorithm combined with the intersection over union(IoU)is used to optimize the anchor parameters in the structure of the region proposal network(RPN),which makes the generated anchor scheme more targeted.The experimental results show that the improved Faster RCNN achieves a mean average precision(mAP)value of 93.4％and a detection speed of 21.79 frame per second on this PCB defect dataset.The proposed method can be generalized to the online detection of tiny defects on the surface of chips and optical devices to improve the efficiency of industrial production.
万方数据
维普
