Data-Efficient Deep Learning for Printed Circuit Board Defect Detection Using X-Ray Images

Automated Optical Inspection (AOI) is widely used by manufacturers for the detection of defects in Printed Circuit boards (PCBs). Recent works have proposed to apply deep learning for defect detection, which is much faster and cheaper than manual inspection. However, AOI can only capture defects on the outmost layers of PCBs using cameras, while modern high-speed circuit PCBs usually have multiple internal layers that need to be inspected. Compared to optical sensors, X-ray tomography provides non-invasive imaging results of all PCB layers. Though one can directly apply an off-the-shelf deep detection model trained on optical domains for X-ray imagery, we show that it usually leads to much lower accuracies in practice. The degraded performance is mainly due to the relatively low quality of X-ray imaging results and the gaps between optical and X-ray modalities. Furthermore, no X-ray PCB image dataset is publicly available for training deep defect detectors. To this end, we propose a novel dataset for X-ray PCB defect detection, dubbed XD-PCB. In XD-PCB, we provide a benchmark for training X-ray automated defect detection models containing synthesized X-ray images and real X-ray images with real defects. However, in a practical environment, retraining the deep model for every unseen X-ray domain is inefficient due to the domain gaps created by different X-ray machine settings and the scarcity of defects. Thus, we propose a domain adaptation framework, dubbed FDX, to improve the efficiency of X-ray PCB defect detection methods. By minimizing the differences between the deep features extracted from abundant training images and the scarce unseen images, we improve the model’s performance in a practical situation, thus enhancing the generalization ability and efficiency of deep detection algorithms when exposed to unseen domains. Our results demonstrate that XD-PCB provides a valuable training baseline for X-ray PCB defect detection, and our proposed FDX framework can effectively increase the popular deep learning model by achieving an increment of 10% in terms of APs compared to other adaptation methods.