Collaborative Learning Classification Model for PCBs Defect Detection Against Image and Label Uncertainty

Surface defect detection of printed circuit boards (PCBs) is a critical stage in ensuring product quality on production lines in electronics manufacturing. The excellent performance of defect detection methods using deep learning models such as convolutional neural networks (CNNs) and autoencoders is limited by image uncertainty under uneven ambient light or unstable transmission channels and label uncertainty due to human perception errors or lack of expert knowledge. To overcome these difficulties, a novel collaborative learning classification model is proposed for surface defect detection on PCBs. An auxiliary inference model is designed to deal with label uncertainty. Then, a symmetric residual filter is set up based on a multiscale symmetric convolutional network to remove image uncertainty in the dataset. At the same time, knowledge-transfer-based probabilistic classification is used to improve the efficiency and performance of the model in different defect detection. Furthermore, a cooperative joint probabilistic inference engine is used to improve the efficiency of the model effectively. Results on both public datasets and self-collected datasets show that the proposed model achieves excellent performance on various quantitative metrics and is suitable for defect detection on datasets collected in complex industrial environments.