JRCC-Net: A Segmentation Network with Joint Representation and Contrast Clustering for Surface Anomaly Detection

The goal of unsupervised surface anomaly detection is to detect areas of the image that are different from the normal pattern, which can be considered as a semantic segmentation problem oriented to anomalous patterns. However, this problem is challenging due to the lack of actual available anomaly samples. In this paper, we transform unsupervised anomaly detection into a self-supervised problem by the proposed anomaly simulation strategy. Using only normal samples for training, real anomalies appearing in the inference phase can be detected. Thus, we propose a segmentation network with joint representation and contrast clustering (JRCC-Net). The proposed method learns a joint representation between simulated samples and the magnitude of the differences in distance from their nearest memory samples, as well as a decision boundary between normal and anomalous samples. Moreover, we propose a novel contrast clustering based representation learning strategy, which allows the model to better learn general patterns from normal samples and mine the latent differences between simulated anomalous and normal samples. JRCC-Net is able to locate anomalies directly in an end-to-end manner and can be trained well with our elaborate anomaly simulation strategy. On the challenging MVTec AD dataset, JRCC-Net outperforms the state-of-the-art unsupervised methods, achieving 98.2 % image-level AUROC and 97.7 % pixel-level AUROC, respectively, and even on the extensively used DAGM dataset, its localization accuracy greatly exceeds that of fully-supervised methods.