Skip to main content

Multiresolution Knowledge Distillation and Multi-level Fusion for Defect Detection

  • Conference paper
  • First Online:
Green, Pervasive, and Cloud Computing (GPC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13744))

Included in the following conference series:

  • 388 Accesses

Abstract

Defect detection has a wide range of applications in industry, and previous work has tended to be supervised learning, which typically requires a large number of samples. In this paper, we propose an unsupervised learning method that learns knowledge about normal images by distilling knowledge from a pre-trained expert network on ImageNet to a learner network of the same size. For a given input image, we use the differences in the features of the different layers of the expert network and learner network to detect and localize defects. We show that using comprehensive knowledge makes the differences between the two networks more apparent and that combining the differences in multi-level features can make the networks more generalizable. It's worth noting that we don't need to split the picture into patches to train, and we don't need to design the learner network additionally. Our general framework is relatively simple, yet has a good detection effect. We provide very competitive results on the MVTecAD dataset and DAGM dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bergmann, P, Fauser, M, Sattlegger, D, et al.: 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)

    Google Scholar 

  2. Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018)

    Article  Google Scholar 

  3. Bergmann, P., et al.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. arXiv preprint. arXiv, 1807.02011 (2018)

    Google Scholar 

  4. Collin, A.S, De Vleeschouwer, C.: Improved anomaly detection by training an autoencoder with skip connections on images corrupted with stain-shaped noise. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp. 7915–7922 (2021)

    Google Scholar 

  5. Dehaene, D, Frigo, O, Combrexelle, S, et al.: Iterative energy-based projection on a normal data manifold for anomaly localization. arXiv preprint. arXiv, 2002.03734 (2020)

    Google Scholar 

  6. Salehi, M., Eftekhar, A., Sadjadi N, et al.: Puzzle-ae: novelty detection in images through solving puzzles. arXiv preprint. arXiv, 2008.12959 (2020)

    Google Scholar 

  7. Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39

    Chapter  Google Scholar 

  8. Schlegl, T., Seeböck, P., Waldstein, S.M., et al.: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019)

    Article  Google Scholar 

  9. Perera, P., Nallapati, R., Xiang, B.: Ocgan: one-class novelty detection using gans with constrained latent representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898–2906 (2019)

    Google Scholar 

  10. Sabokrou, M., Khalooei, M., Fathy, M., et al.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3379–3388 (2018)

    Google Scholar 

  11. Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint. arXiv 1701.04862 (2017)

    Google Scholar 

  12. Salimans, T., Goodfellow, I., Zaremba, W., et al.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems, vol. 29 (2016)

    Google Scholar 

  13. Wang, S., Zeng, Y., Liu, X., et al.: Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  14. Fei, Y., Huang, C., Jinkun, C., et al.: Attribute restoration framework for anomaly detection. IEEE Trans. Multimedia 24, 116–127 (2020)

    Google Scholar 

  15. Bergmann, P., Fauser, M., Sattlegger, D., et al.: Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4183–4192 (2020)

    Google Scholar 

  16. Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better?. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2661–2671 (2019)

    Google Scholar 

  17. Sun, R., Zhu, X., Wu, C., et al.: Not all areas are equal: transfer learning for semantic segmentation via hierarchical region selection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,. pp. 4360–4369 (2019)

    Google Scholar 

  18. Salehi, M., Sadjadi, N., Baselizadeh, S., et al.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14902–14912 (2021)

    Google Scholar 

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

    Google Scholar 

  20. Deng, J., Dong, W., Socher, R., et al.: Imagenet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 248–255 (2009)

    Google Scholar 

  21. Carrara, F., Amato, G., Brombin, L., et al.: Combining gans and autoencoders for efficient anomaly detection. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp. 3939–3946 (2021)

    Google Scholar 

  22. Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

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

    Article  Google Scholar 

  24. Yi, J., Yoon, S.: Patch svdd: patch-level svdd for anomaly detection and segmentation. In: Proceedings of the Asian Conference on Computer Vision (2020)

    Google Scholar 

  25. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Machine Learn. 54(1), 45–66 (2004). https://doi.org/10.1023/B:MACH.0000008084.60811.49

    Article  MATH  Google Scholar 

  26. Ruff, L., Vandermeulen, R., Goernitz, N., et al.: Deep one-class classification. In: International Conference on Machine Learning. PMLR, pp. 4393–4402 (2018)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  29. Wang, G., Han, S., Ding, E., et al.: Student-teacher feature pyramid matching for unsupervised anomaly detection. arXiv preprint. arXiv 2103.04257 (2021)

    Google Scholar 

  30. Venkataramanan, S., Peng, K.-C., Singh, R.V., Mahalanobis, A.: Attention guided anomaly localization in images. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 485–503. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58520-4_29

    Chapter  Google Scholar 

  31. Zhang, R., Isola, P., Efros, A.A., et al.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)

    Google Scholar 

  32. Wieler, M., Hahn, T.: Weakly supervised learning for industrial optical inspection. In: DAGM Symposium In (2007)

    Google Scholar 

Download references

Acknowledgements

This research was partially funded by Xianyang Science and Technology Research PlanProject (2021ZDYF-NY-O014) and Xi’an Science and Technology Plan Project (2022JH-JSYF-O270). All supports and assistance are sincerely appreciated

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huosheng Xie .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xie, H., Xiao, Y. (2023). Multiresolution Knowledge Distillation and Multi-level Fusion for Defect Detection. In: Yu, C., Zhou, J., Song, X., Lu, Z. (eds) Green, Pervasive, and Cloud Computing. GPC 2022. Lecture Notes in Computer Science, vol 13744. Springer, Cham. https://doi.org/10.1007/978-3-031-26118-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26118-3_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26117-6

  • Online ISBN: 978-3-031-26118-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics