Abstract: In the production of printed circuit boards (PCB), the production process and other problems incur flaws and defects on the circuit board. In order to enhance the detection efficiency of circuit board defects, a circuit board defect detection network based on convolutional neural network (CNN) was proposed. The whole detection network was optimized and reconstructed based on the YOLO v4 network. Aiming at the difficulty of precise and complex PCB production and difficult detection of various defects, a long-distance global attention mechanism based on fine-grained spatial domain was added to the optimized network. At the same time, on the basis of the spatial pyramid pooling (SPP) module, the feature map was reorganized as the input of each YOLO detection head. The long-distance attention mechanism channel was adopted to transfer the features extracted from the shallow network to the deep network, and the feature map reorganization method was utilized to boost the richness of feature information, thereby improving the accuracy of PCB defect detection. After experimental analysis, compared with various classic convolutional neural networks, the proposed algorithm is greatly superior in PCB board defect detection tasks. The mean average precision (mAP) of the overall defects reaches 91.40%, which is suitable for actual production and testing links. 

Key words: printed circuit board, deep learning, convolutional neural network, defect detection, attention mechanism