Center-aware Residual Anomaly Synthesis for Multi-class 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 multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class 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 multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class 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. The source code and the newly constructed dataset are publicly available atthis https URL.

@article{chen2025_2505.17551,
  title={ Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection },
  author={ Qiyu Chen and Huiyuan Luo and Haiming Yao and Wei Luo and Zhen Qu and Chengkan Lv and Zhengtao Zhang },
  journal={arXiv preprint arXiv:2505.17551},
  year={ 2025 }
}