Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.

Algorithmic Scheme for Concurrent Detection and Classification of Printed Circuit Board Defects

An ideal printed circuit board(PCB)defect inspection system can detect defects and classify PCB defect types.Existing defect inspection technologies can identify defects but fail to classify all PCB defect types.This research thus proposes an algorithmic scheme that can detect and categorize all 14-known PCB defect types.In the proposed algorithmic scheme,fuzzy cmeans clustering is used for image segmentation via image subtraction prior to defect detection.Arithmetic and logic operations,the circle hough transform(CHT),morphological reconstruction(MR),and connected component labeling(CCL)are used in defect classification.The algorithmic scheme achieves 100%defect detection and 99.05%defect classification accuracies.The novelty of this research lies in the concurrent use of CHT,MR,and CCL algorithms to accurately detect and classify all 14-known PCB defect types and determine the defect characteristics such as the location,area,and nature of defects.This information is helpful in electronic parts manufacturing for finding the root causes of PCB defects and appropriately adjusting the manufacturing process.Moreover,the algorithmic scheme can be integrated into machine vision to streamline the manufacturing process,improve the PCB quality,and lower the production cost.