Center-Aware Residual Anomaly Synthesis for Multiclass Industrial Anomaly Detection

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multiclass anomaly detection. However, the significant increase in interclass interference leads to severe missed detections. Furthermore, the intraclass overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel center-aware residual anomaly synthesis (CRAS) method for multiclass anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of interclass interference. To further reduce intraclass overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS.