Surface Anomaly Detection With Anomalous Feature Restriction And Difference-Aware Enhancement

In industrial automatic product quality inspection, visual anomaly detection is paramount. While unsupervised anomaly detection methods based on reconstruction have shown promising results, particularly in anomaly localization, these methods still suffer from challenges such as overfitting of pseudo-anomalous distribution by reconstruction networks and difficulty in distinguishing near-distribution anomalies by discriminative networks. In this paper, we propose a novel Anomalous Feature Restriction and Difference-Aware Enhancement Network (RE-Net), which aims to constrain abnormal features while enhancing the minute discrepancies between normal and abnormal features. This network comprises two key modules: the Abnormal Feature Restriction Module (AFRM) and the Difference-Aware Enhancement Module (DAEM). AFRM first explicitly constrains abnormal features by utilizing normal features in the reconstructed subnetwork to prevent the network from overfitting pseudo-abnormal distributions while ensuring the consistency of normal regions. Upon achieving a normal reconstruction from an anomalous input, DAEM is then used to enhance the perception of the difference between normal and abnormal in the discriminant subnetwork, thereby effectively improving the detection ability of highly camouflaged near-distribution anomalies. A series of comparative experiments on textured objects in the MVTec AD dataset show that our method achieves better anomaly detection results, reaching 99.9% image-level AUROC and 98.76% pixel-level AUROC.