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LGFDR: local and global feature denoising reconstruction for unsupervised anomaly detection

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Abstract

Unsupervised anomaly detection is a challenging task in many visual inspection scenarios and has attracted significant attention. Anomalies are typically related to local low-level features or require global semantic information to be detected. However, most of the existing methods fail to strike a balance between local and global features and thus lack versatility and practicality. To address this issue, we propose local and global feature denoising reconstruction (LGFDR). The proposed method can implicitly learn the latent distribution of local and global features for normal images via a dual-tower reconstruction network. Next, a selective reconstruction head (SRH) is designed to adaptively fuse the information from local and global reconstructions. Moreover, adding noise to the features proves a simple and general operation that can further enhance the generalization of reconstruction networks. On the MVTec AD benchmark, LGFDR achieves 98.8% and 65.3% of pixel-level AUROC and AP for anomaly localization and 99.3% of image-level AUROC for anomaly detection, respectively. In addition, a real-world metal plate surface defect detection project is adopted to validate LGFDR. Both the public and the practical experimental results show the effectiveness of our proposed approach. The code will be available at https://github.com/Karma1628/work-1.

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References

  1. Gaus, Y.F.A., Bhowmik, N., Akçay, S., Guillén-Garcia, P.M., Barker, J.W., Breckon, T.P.: Evaluation of a dual convolutional neural network architecture for object-wise anomaly detection in cluttered x-ray security imagery. In: 2019 International Joint Conference on Neural Networks, pp. 1–8 (2019)

  2. Tao, X., Zhang, D., Ma, W., Hou, Z., Lu, Z., Adak, C.: Unsupervised anomaly detection for surface defects with dual-siamese network. IEEE Trans. Ind. Inform. 18(11), 7707–7717 (2022)

    Article  Google Scholar 

  3. Lüth, C.T., Zimmerer, D., Koehler, G., Jaeger, P.F., Isenensee, F., Maier-Hein, K.H.: Contrastive representations for unsupervised anomaly detection and localization. In: BVM Workshop, pp. 246–252 (2023)

  4. Tao, X., Gong, X., Zhang, X., Yan, S., Adak, C.: Deep learning for unsupervised anomaly localization in industrial images: a survey. IEEE Trans. Instrum. Meas. 71, 1–21 (2022)

    Google Scholar 

  5. Liu, J., Xie, G., Wang, J., Li, S., Wang, C., Zheng, F., Jin, Y.: Deep industrial image anomaly detection: a survey. arXiv preprint, 2 (2023). arXiv:2301.11514

  6. Roth, K., Pemula, L., Zepeda, J., Schölkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14298–14308 (2022)

  7. Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: Pattern Recognition. ICPR International Workshops and Challenges, pp. 475–489 (2021)

  8. Yu, J., Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., Wu, L.: Fastflow: unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint (2021). arXiv:2111.07677

  9. Lei, J., Hu, X., Wang, Y., Liu, D.: Pyramidflow: high-resolution defect contrastive localization using pyramid normalizing flow. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14143–14152 (2023)

  10. Liu, Z., Zhou, Y., Xu, Y., Wang, Z.: Simplenet: a simple network for image anomaly detection and localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20402–20411 (2023)

  11. Shi, Y., Yang, J., Qi, Z.: Unsupervised anomaly segmentation via deep feature reconstruction. Neurocomputing 424, 9–22 (2021)

    Article  Google Scholar 

  12. Zavrtanik, V., Kristan, M., Skočaj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recognit. 112, 107706 (2021)

    Article  Google Scholar 

  13. Akçay, S., Atapour-Abarghouei, A., Breckon, T.P.: Skip-ganomaly: skip connected and adversarially trained encoder-decoder anomaly detection. In: 2019 International Joint Conference on Neural Networks, pp. 1–8 (2019)

  14. 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 (2019)

  15. You, Z., Yang, K., Luo, W., Cui, L., Zheng, Y., Le, X.: Adtr: anomaly detection transformer with feature reconstruction. In: International Conference on Neural Information Processing, pp. 298–310. Springer (2022)

  16. Chen, L., You, Z., Zhang, N., Xi, J., Le, X.: Utrad: anomaly detection and localization with u-transformer. Neural Netw. 147, 53–62 (2022)

    Article  Google Scholar 

  17. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

  18. Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint (2020). arXiv:2005.02357

  19. Rudolph, M., Wandt, B., Rosenhahn, B.: Same same but differnet: semi-supervised defect detection with normalizing flows. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1907–1916 (2021)

  20. Song, J., Kong, K., Park, Y-I., Kim, S-G., Kang, S-J.: Anoseg: anomaly segmentation network using self-supervised learning. arXiv preprint arXiv:2110.03396 (2021)

  21. Liang, Y., Zhang, J., Zhao, S., Runze, W., Liu, Y., Pan, S.: Omni-frequency channel-selection representations for unsupervised anomaly detection. IEEE Trans. Image Process. 32, 4327–4340 (2023)

    Article  Google Scholar 

  22. Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103 (2008)

  23. 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)

  24. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

  25. Woo, S., Park, J., Lee, J-Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (2018)

  26. Yang, L., Zhang, R-Y., Li, L., Xie, X.: Simam: a simple, parameter-free attention module for convolutional neural networks. In: International Conference on Machine Learning, pp. 11863–11874 (2021)

  27. Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13708–13717 (2021)

  28. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11531–11539 (2020)

  29. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint (2014). arXiv:1409.1556

  30. 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 (2016)

  31. Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint (2016) arXiv:1605.07146

  32. Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Chaudhuri, K., Salakhutdinov, R., (eds) Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 6105–6114 (2019)

  33. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint (2020). arXiv:2010.11929

  34. Wang, W., Xie, E., Li, X., Fan, D-P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 568–578 (2021)

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Acknowledgements

This research is supported by the Natural Science Foundation of Chongqing under Grant (CSTB2022NSCQ-MSX0922) and the Science and Technology Project of Shenzhen under Grant (GXWD-20220811170603002).

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Authors and Affiliations

  1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610041, Sichuan, China

    Yichi Chen & Min Chen

  2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China

    Yichi Chen, Bin Chen & Min Chen

  3. International Institute for Art. Intelligence, HIT, Shenzhen, 518000, Guangdong, China

    Bin Chen & Junjie Wang

  4. Chongqing Research Institute of Harbin Institute of Technology, HIT, Chongqing, 610101, China

    Bin Chen & Weizhi Xian

  5. Department of Test and Inspection, Shanghai Spaceflight Precision Machinery Institute, Shanghai, 610101, China

    Yao Huang

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  1. Yichi Chen
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  2. Bin Chen
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  4. Junjie Wang
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Correspondence to Bin Chen.

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Chen, Y., Chen, B., Xian, W. et al. LGFDR: local and global feature denoising reconstruction for unsupervised anomaly detection. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03281-x

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