Adversarial semi‐supervised learning method for printed circuit board unknown defect detection

Abstract

Due to the lack of training data and fuzziness of unknown defects, unknown defect detection, which aims to identify no clearly defined defects, is still a challenging task. In practical industrial scenarios, defects on a printed circuit board account generally for a small proportion, so the data sets are highly biased towards no defect class. To this end, unknown defect detection can be treated as an anomaly detection problem. According to this, a semi-supervised learning method is proposed in this study to solve the above-mentioned problems. Inspired by the conditional generative adversarial network, the authors propose an improved end-to-end architecture for detecting unknown defects. The designed architecture is composed of three networks: a generator, a discriminator, and an encoder. Among them, the generator and the discriminator are trained by competing with each other, while collaborating to learn the distribution of underlying concepts in the target class. During training, the authors only train normal samples, and unknown defects do not appear in the process. In the testing phase, unknown defects are detected by calculating the distance between generated samples and real samples under the feature space. Experimental results over several benchmark data sets show the effectiveness of the model and superiority on state-of-the-art approaches.