PCB defect detection algorithm for domestic industrial cameras based on deep learning
In response to the problem of printed circuit board(PCB)defect detection,which imported complete set of systems are expensive,closed-source,and unsupported secondary development,at same time,the efficiency and accuracy of the core detection algorithms for domestic industrial cameras hardware are poor,a PCB defect detection algorithm for domestic industrial cameras based on deep learning was proposed.Firstly,the PCB training sample set was collected by domestic industrial cameras,then generated an anchor box that meets the defect size according to the defect characteristics by the K-means++ algorithm.Secondly,a feature layer with a scale of 104×104 was added to extract more scale feature information based on the YOLOv3 network structure for deep learning algorithm.Finally,the defect location and category were obtained by the joint prediction of multi-scale features.The experimental results show that the mean Average Precision of the proposed algorithm is 97.42%,which better than SSD,YOLOv3 and Faster RCNN algorithm at same level of time cost,and can meet the actual needs of PCB defect detection for domestic industrial cameras.
domestic industrial cameraprinted circuit board defect detectionYOLOv3K-means++multi-scale features
国家科技期刊平台
NSTL
万方数据
维普
