FABLE : Fabric Anomaly Detection Automation Process (2306.10089v1)
Abstract: Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. The vast majority of industry-oriented methods focus on learning from good samples to detect anomaly notwithstanding some specific industrial scenario requiring even less specific training and therefore a generalization for anomaly detection. The obvious use case is the fabric anomaly detection, where we have to deal with a really wide range of colors and types of textile and a stoppage of the production line for training could not be considered. In this paper, we propose an automation process for industrial fabric texture defect detection with a specificity-learning process during the domain-generalized anomaly detection. Combining the ability to generalize and the learning process offer a fast and precise anomaly detection and segmentation. The main contributions of this paper are the following: A domain-generalization texture anomaly detection method achieving the state-of-the-art performances, a fast specific training on good samples extracted by the proposed method, a self-evaluation method based on custom defect creation and an automatic detection of already seen fabric to prevent re-training.
- “MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection” In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Long Beach, CA, USA: IEEE, 2019, pp. 9584–9592 DOI: 10.1109/CVPR.2019.00982
- “Student-Teacher Feature Pyramid Matching for Anomaly Detection” In arXiv:2103.04257 [cs], 2021 arXiv: http://arxiv.org/abs/2103.04257
- “Episodic Training for Domain Generalization” In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) Seoul, Korea (South): IEEE, 2019, pp. 1446–1455 DOI: 10.1109/ICCV.2019.00153
- “Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization” arXiv, 2020 arXiv: http://arxiv.org/abs/2007.09316
- “Domain-Generalized Textured Surface Anomaly Detection” arXiv, 2022 arXiv: http://arxiv.org/abs/2203.12304
- “ADBench: Anomaly Detection Benchmark” arXiv, 2022 arXiv: http://arxiv.org/abs/2206.09426
- Shuang Mei, Yudan Wang and Guojun Wen “Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model” In Sensors 18.4, 2018, pp. 1064 DOI: 10.3390/s18041064
- “GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection” In arXiv:1903.06661 [cs, stat], 2019 arXiv: http://arxiv.org/abs/1903.06661
- Vitjan Zavrtanik, Matej Kristan and Danijel Skočaj “DRAEM – A discriminatively trained reconstruction embedding for surface anomaly detection” arXiv, 2021 arXiv: http://arxiv.org/abs/2108.07610
- “Generative Adversarial Networks” In arXiv:1406.2661 [cs, stat], 2014 arXiv: http://arxiv.org/abs/1406.2661
- “f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks” In Medical Image Analysis 54, 2019, pp. 30–44 DOI: 10.1016/j.media.2019.01.010
- “G2D: Generate to Detect Anomaly” event-place: Waikoloa, HI, USA In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) IEEE, 2021, pp. 2002–2011 DOI: 10.1109/WACV48630.2021.00205
- “Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection” In arXiv:2203.00259 [cs], 2022 arXiv: http://arxiv.org/abs/2203.00259
- “Deep Residual Learning for Image Recognition” arXiv, 2015 arXiv: http://arxiv.org/abs/1512.03385
- Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton “ImageNet classification with deep convolutional neural networks” In Communications of the ACM 60.6, 2017, pp. 84–90 DOI: 10.1145/3065386
- Mingxing Tan and Quoc V. Le “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks” arXiv, 2020 arXiv: http://arxiv.org/abs/1905.11946
- “FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows” In arXiv:2111.07677 [cs], 2021 arXiv: http://arxiv.org/abs/2111.07677
- “Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection” In arXiv:2110.02855 [cs], 2021 arXiv: http://arxiv.org/abs/2110.02855
- “Towards Total Recall in Industrial Anomaly Detection” In arXiv:2106.08265 [cs], 2021 arXiv: http://arxiv.org/abs/2106.08265
- “Asymmetric Student-Teacher Networks for Industrial Anomaly Detection” arXiv, 2022 arXiv: http://arxiv.org/abs/2210.07829
- Sungwook Lee, Seunghyun Lee and Byung Cheol Song “CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization” arXiv, 2022 arXiv: http://arxiv.org/abs/2206.04325
- Simon Thomine, Hichem Snoussi and Mahmoud Soua “MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection”, 2023
- “Fine-Grained Material Classification Using Micro-geometry and Reflectance” Series Title: Lecture Notes in Computer Science In Computer Vision – ECCV 2016 9909 Cham: Springer International Publishing, 2016, pp. 778–792 DOI: 10.1007/978-3-319-46454-1˙47
- “Describing Textures in the Wild” In 2014 IEEE Conference on Computer Vision and Pattern Recognition Columbus, OH, USA: IEEE, 2014, pp. 3606–3613 DOI: 10.1109/CVPR.2014.461
- Henry Y. T. Ngan, Grantham K. H. Pang and Nelson H. C. Yung “Automated fabric defect detection—A review” In Image and Vision Computing 29.7, 2011, pp. 442–458 DOI: 10.1016/j.imavis.2011.02.002
- “Anomalib: A Deep Learning Library for Anomaly Detection” arXiv, 2022 arXiv: http://arxiv.org/abs/2202.08341