CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection

• URL: http://arxiv.org/abs/2408.15628v2
• Date: Sun, 1 Sep 2024 13:22:03 GMT
• Title: CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection
• Authors: Yu-Hsuan Hsieh, Shang-Hong Lai,
• Abstract summary: We develop an unsupervised component segmentation technique that generates training labels for a lightweight segmentation network without human labeling. We achieve a detection AUROC of 95.3% in the MVTec LOCO AD dataset, which surpasses previous SOTA methods.
• Score: 10.716585855033347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To improve logical anomaly detection, some previous works have integrated segmentation techniques with conventional anomaly detection methods. Although these methods are effective, they frequently lead to unsatisfactory segmentation results and require manual annotations. To address these drawbacks, we develop an unsupervised component segmentation technique that leverages foundation models to autonomously generate training labels for a lightweight segmentation network without human labeling. Integrating this new segmentation technique with our proposed Patch Histogram module and the Local-Global Student-Teacher (LGST) module, we achieve a detection AUROC of 95.3% in the MVTec LOCO AD dataset, which surpasses previous SOTA methods. Furthermore, our proposed method provides lower latency and higher throughput than most existing approaches.

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