AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios

• URL: http://arxiv.org/abs/2410.14379v1
• Date: Fri, 18 Oct 2024 11:07:12 GMT
• Title: AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
• Authors: Ziming Huang, Xurui Li, Haotian Liu, Feng Xue, Yuzhe Wang, Yu Zhou,
• Abstract summary: AnomalyNCD is a multi-class anomaly classification framework compatible with existing anomaly detection methods. It learns anomaly-specific features and classifies anomalies in a self-supervised manner. Our method outperforms the state-of-the-art works on the MVTec AD and MTD datasets.
• Score: 16.77348120041789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the industrial scenario, anomaly detection could locate but cannot classify anomalies. To complete their capability, we study to automatically discover and recognize visual classes of industrial anomalies. In terms of multi-class anomaly classification, previous methods cluster anomalies represented by frozen pre-trained models but often fail due to poor discrimination. Novel class discovery (NCD) has the potential to tackle this. However, it struggles with non-prominent and semantically weak anomalies that challenge network learning focus. To address these, we introduce AnomalyNCD, a multi-class anomaly classification framework compatible with existing anomaly detection methods. This framework learns anomaly-specific features and classifies anomalies in a self-supervised manner. Initially, a technique called Main Element Binarization (MEBin) is first designed, which segments primary anomaly regions into masks to alleviate the impact of incorrect detections on learning. Subsequently, we employ mask-guided contrastive representation learning to improve feature discrimination, which focuses network attention on isolated anomalous regions and reduces the confusion of erroneous inputs through re-corrected pseudo labels. Finally, to enable flexible classification at both region and image levels during inference, we develop a region merging strategy that determines the overall image category based on the classified anomaly regions. Our method outperforms the state-of-the-art works on the MVTec AD and MTD datasets. Compared with the current methods, AnomalyNCD combined with zero-shot anomaly detection method achieves a 10.8% $F_1$ gain, 8.8% NMI gain, and 9.5% ARI gain on MVTec AD, 12.8% $F_1$ gain, 5.7% NMI gain, and 10.8% ARI gain on MTD. The source code is available at https://github.com/HUST-SLOW/AnomalyNCD.

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