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

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

23 May 2025arXiv:2505.17551archive 2025-07-28

Qiyu Chen, Huiyuan Luo, Haiming Yao, Wei Luo, Zhen Qu, Chengkan Lv, Zhengtao Zhang

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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cqylunlun/CRAS officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Anomaly DetectionMulti-class Anomaly Detection

Datasets

Introduced by this paper, per the archive.

ITDD

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Anomaly Detection ITDD CRAS Detection AUROC 99.6 #1 of 1 Archive leaderboard report
Anomaly Detection ITDD CRAS Segmentation AUROC 98.0 #1 of 1 Archive leaderboard report
Anomaly Detection MPDD CRAS Detection AUROC 98.8 #2 of 16 Archive leaderboard report
Anomaly Detection MPDD CRAS Segmentation AUROC 98.7 #2 of 16 Archive leaderboard report
Anomaly Detection MVTec AD CRAS Detection AUROC 99.7 #15 of 148 Archive leaderboard report
Anomaly Detection MVTec AD CRAS Segmentation AUROC 98.4 #15 of 148 Archive leaderboard report
Anomaly Detection VisA CRAS Detection AUROC 97.0 #18 of 50 Archive leaderboard report
Anomaly Detection VisA CRAS Segmentation AUROC 98.4 #18 of 50 Archive leaderboard report
Multi-class Anomaly Detection ITDD CRAS Detection AUROC 99.4 #1 of 1 Archive leaderboard report
Multi-class Anomaly Detection ITDD CRAS Segmentation AUROC 97.8 #1 of 1 Archive leaderboard report
Multi-class Anomaly Detection MVTec AD CRAS Detection AUROC 98.3 #9 of 13 Archive leaderboard report
Multi-class Anomaly Detection MVTec AD CRAS Segmentation AUROC 98.0 #9 of 13 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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