Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks

• URL: http://arxiv.org/abs/2207.09792v2
• Date: Thu, 15 Aug 2024 03:25:15 GMT
• Title: Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks
• Authors: Jianfeng Huang, Chenyang Li, Yimin Lin, Shiguo Lian,
• Abstract summary: We propose Vision Transformer based (VIT) unsupervised anomaly detection network. It utilizes a hierarchical task learning and human experience to enhance its interpretability. Our method achieves 99.8% AUC, which surpasses previous state-of-the-art methods.
• Score: 6.393288885927437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is hard to collect enough flaw images for training deep learning network in industrial production. Therefore, existing industrial anomaly detection methods prefer to use CNN-based unsupervised detection and localization network to achieve this task. However, these methods always fail when there are varieties happened in new signals since traditional end-to-end networks suffer barriers of fitting nonlinear model in high-dimensional space. Moreover, they have a memory library by clustering the feature of normal images essentially, which cause it is not robust to texture change. To this end, we propose the Vision Transformer based (VIT-based) unsupervised anomaly detection network. It utilizes a hierarchical task learning and human experience to enhance its interpretability. Our network consists of pattern generation and comparison networks. Pattern generation network uses two VIT-based encoder modules to extract the feature of two consecutive image patches, then uses VIT-based decoder module to learn the human designed style of these features and predict the third image patch. After this, we use the Siamese-based network to compute the similarity of the generation image patch and original image patch. Finally, we refine the anomaly localization by the bi-directional inference strategy. Comparison experiments on public dataset MVTec dataset show our method achieves 99.8% AUC, which surpasses previous state-of-the-art methods. In addition, we give a qualitative illustration on our own leather and cloth datasets. The accurate segment results strongly prove the accuracy of our method in anomaly detection.

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