Abstract
Detection of subtle surface anomalies in the presence of strong noise is a challenging vision task. This paper presents a new neural network called MemFlowNet for detecting subtle surface anomalies by combining the advantages of the memory bank and normalizing flow. The proposed method consists of two stages. The first stage achieves pixel-level segmentation of anomalies using noise-insensitive average features in the memory bank and Nearest Neighbor search strategy, and the second stage performs image-level detection using normalizing flows and multi-scale score fusion. A new dataset called INSCup has been developed to assist this research by acquiring inner surface images of stainless steel insulated cups with ultra-wide lenses. The performance of MemFlowNet has been validated on INSCup dataset by surpassing other mainstream methods. In addition, MemFlowNet achieves the best performance with an image-level AUROC of 99.57% in anomaly detection of MVTec-AD benchmark. It shows a great potential to apply MemFlowNet to automated visual inspection of surface anomalies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Open source implementation is available at github.com/openvinotoolkit/anomalib.
References
Akcay, S., Ameln, D., Vaidya, A., Lakshmanan, B., Ahuja, N., Genc, U.: Anomalib: a deep learning library for anomaly detection (2022)
Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD-a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592–9600 (2019)
Defard, T., Setkov, A., Loesch, A., Audigier, R.: PaDiM: a patch distribution modeling framework for anomaly detection and localization. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12664, pp. 475–489. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68799-1_35
Dinh, L., Krueger, D., Bengio, Y.: NICE: non-linear independent components estimation. arXiv preprint arXiv:1410.8516 (2014)
Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP. arXiv preprint arXiv:1605.08803 (2016)
Grishin, A., BorisV: Severstal: steel defect detection (2019). https://kaggle.com/competitions/severstal-steel-defect-detection
Gudovskiy, D., Ishizaka, S., Kozuka, K.: CFLOW-AD: real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 98–107 (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile. Vis. Comput. 36, 85–96 (2020)
Kim, Y., Jang, H., Lee, D., Choi, H.J.: AltUB: alternating training method to update base distribution of normalizing flow for anomaly detection. arXiv preprint arXiv:2210.14913 (2022)
Lee, S., Lee, S., Song, B.C.: CFA: coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. IEEE Access 10, 78446–78454 (2022)
Li, C.L., Sohn, K., Yoon, J., Pfister, T.: CutPaste: self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664–9674 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Rizzi, A., Gatta, C., Marini, D.: A new algorithm for unsupervised global and local color correction. Pattern Recogn. Lett. 24(11), 1663–1677 (2003)
Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318–14328 (2022)
Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., Müller, E., Kloft, M.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393–4402. PMLR (2018)
Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285, 858–864 (2013)
Tabernik, D., Šela, S., Skvarč, J., Skočaj, D.: Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 31(3), 759–776 (2020)
Yang, M., Wu, P., Feng, H.: MemSeg: a semi-supervised method for image surface defect detection using differences and commonalities. Eng. Appl. Artif. Intell. 119, 105835 (2023)
Yu, J., et al.: Fastflow: unsupervised anomaly detection and localization via 2D normalizing flows. arXiv preprint arXiv:2111.07677 (2021)
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
Zavrtanik, V., Kristan, M., Skočaj, D.: DRAEM-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8330–8339 (2021)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huang, L., Li, F., Li, D., Zhang, M. (2024). MemFlowNet: A Network for Detecting Subtle Surface Anomalies with Memory Bank and Normalizing Flow. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_33
Download citation
DOI: https://doi.org/10.1007/978-981-99-8148-9_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8147-2
Online ISBN: 978-981-99-8148-9
eBook Packages: Computer ScienceComputer Science (R0)