Printed Circuit Board (PCB) Defect Detection Using Deep Learning

With the widespread application of printed circuit boards (PCBs) in electronic manufacturing, the complexity and accuracy of their production processes continue to increase. Traditional manual inspection and automatic optical inspection (AOI) methods are gradually exposed to limitations in large-scale production environments. It is challenging to satisfy the requirements of efficient and high-precision identification of defects. Based on deep learning techniques, this study evaluated four algorithms-convolutional neural networks (CNN), residual networks (ResNet), YOLO, and Faster R-CNN to select a scheme that can achieve fast and precise flaw identification in settings with limited resources. By comparing the performance of each algorithm in the aspects of detection accuracy, computing resource requirement and implementation complexity, YOLO was selected as the defect detection algorithm. With a single-stage inspection architecture and real-time performance, the YOLO algorithm detects the surface defect of PCBs, like missing holes or virtual copper. It’s very suitable for small and medium enterprises (SMEs) in their quality control needs. Meanwhile, it also integrates a simple user interface, providing practical scheme detection for enterprises. The results show that YOLO not only increases the effectiveness and precision of the inspection but also matches the direction of sustainable development in the intelligent manufacturing and optimization of resources.