Few-Sample Anomaly Detection in Industrial Images with Edge Enhancement and Cascade Residual Feature Refinement

In industrial inspection scenarios, the scarcity of data and the varying appearances of anomalies pose significant challenges for existing methods in accurately localizing anomaly edges and reducing detection errors. To address these issues, we propose a few-sample anomaly detection method based on edge enhancement and cascade optimization of residual features. Our approach includes a distribution transformation-based augmentation method to generate a variety of augmented images that closely resemble the distribution of real anomaly images. We introduce an anomaly detection method combined with an edge-guided feature enhancement module and a complementary feature attention module, which accentuates features at the edges of anomalies and emphasizes anomalous regions in a cascaded structure to achieve refined anomaly localization. Extensive experiments on three widely used datasets demonstrate that the proposed approach outperforms state-of-the-art methods in detection and localization accuracy.