Exploring the Relationship Between Samples and Masks for Robust Defect Localization

Defect detection aims to detect and localize regions out of the normal distribution. The previous approaches often explicitly incorporate the defect detection concept, such as by utilizing self-supervised ground truth or manually defined feature comparison. The aforementioned processes involve modeling the distribution of normal samples, and they rely on the modeled normality for accurate inference. This reliance may hinder their ability to generalize to unseen test scenarios or the test set that deviates from the training distribution. In this paper, we propose a one-stage framework that detects defective patterns directly without the modeling process. This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic correction mechanism that allows the network to self-correct. In training, explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping. Experimental results show that the proposed method performs superior in comparison with the previous SOTA methods in various test scenarios.