Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection

• URL: http://arxiv.org/abs/2505.17551v1
• Date: Fri, 23 May 2025 06:56:44 GMT
• Title: Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
• Authors: Qiyu Chen, Huiyuan Luo, Haiming Yao, Wei Luo, Zhen Qu, Chengkan Lv, Zhengtao Zhang,
• Abstract summary: Anomaly detection plays a vital role in the inspection of industrial images.<n>Most existing methods require separate models for each category, resulting in multiplied deployment costs.<n>We propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection.
• Score: 2.3494824114381814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 at https://github.com/cqylunlun/CRAS.

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