Skip to main content
SpringerLink
Log in

Anomaly localization in regular textures based on deep convolutional generative adversarial networks

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Pixel-level anomaly localization is a challenging problem due to the lack of abnormal training samples. The existing adversarial network methods attempt to segment anomalies by reconstructing the image then comparing the reconstructed image with the original. However, reconstructing an image with adversarial networks involve complex training procedures and result in long run-times. This paper proposes a simpler and intuitive anomaly localization approach based on generative adversarial networks (GAN) for regular textured images. In the proposed method, a discriminator network generates an anomaly map and is trained by a generator network that generates imitations of anomalous samples. To lower computational costs, strided convolutions are used in the discriminator network to produce anomaly map for pixel blocks instead of individual pixels. Discriminator that is trained in the proposed scheme gains ability to segment the anomalies in images. The experimental results show that the performance of the proposed method is almost equivalent to that of the state-of-the-art methods. Besides, with an accompanying low-cost training phase it is faster and simpler to implement.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Altmann Y, McLaughlin S, Hero A (2015) Robust linear spectral unmixing using anomaly detection. IEEE Transactions on Computational Imaging 1(2):74–85

    Article  MathSciNet  Google Scholar 

  2. Bergmann P, Fauser M, Sattlegger D, Steger C (2019) Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9592–9600

  3. Bergmann P, Batzner K, Fauser M, Sattlegger D, Steger C (2021) The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. International Journal of Computer Vision 129(4):1038–1059

    Article  Google Scholar 

  4. Bian J, Hui X, Sun S, Zhao X, Tan M (2019) A novel and efficient cvae-gan-based approach with informative manifold for semi-supervised anomaly detection. IEEE Access 7:88903–88916

    Article  Google Scholar 

  5. Carrera D, Manganini F, Boracchi G, Lanzarone E (2017) Defect detection in sem images of nanofibrous materials. IEEE Transactions on Industrial Informatics 13(2):551–561

    Article  Google Scholar 

  6. Chow JK, Su Z, Wu J, Tan PS, Mao X, Wang Y (2020) Anomaly detection of defects on concrete structures with the convolutional autoencoder. Adv Eng Inform 45:101105

    Article  Google Scholar 

  7. Elmoufidi A, El Fahssi K, Jai-andaloussi S, Sekkaki A, Gwenole Q, Lamard M (2018) Anomaly classification in digital mammography based on multiple-instance learning. IET Image Process 12 (3):320–328

    Article  Google Scholar 

  8. Fan Y, Wen G, Li D, Qiu S, Levine MD, Xiao F (2020) Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder. Computer Vision and Image Understanding 195:102920

    Article  Google Scholar 

  9. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. Curran Associates, Inc., pp 2672–2680

  10. Griffin LD, Caldwell M, Andrews JTA, Bohler H (2019) Unexpected item in the bagging area: anomaly detection in x-ray security images. IEEE Transactions on Information Forensics and Security 14(6):1539–1553

    Article  Google Scholar 

  11. Grünbaum B, Shephard GC (1987) Tilings and patterns. Courier Dover Publications

  12. He Y, Song K, Dong H, Yan Y (2019) Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network. Opt Lasers Eng 122:294–302

    Article  Google Scholar 

  13. Hu G, Huang J, Wang Q, Li J, Xu Z, Huang X (2020) Unsupervised fabric defect detection based on a deep convolutional generative adversarial network. Text Res J 90(3-4):247–270. https://doi.org/10.1177/0040517519862880

    Article  Google Scholar 

  14. Iakovidis DK, Georgakopoulos SV, Vasilakakis M, Koulaouzidis A, Plagianakos VP (2018) Detecting and locating gastrointestinal anomalies using deep learning and iterative cluster unification. IEEE Transactions on Medical Imaging 37(10):2196–2210

    Article  Google Scholar 

  15. Ji Q, Chi R, Lu Z (2018) Anomaly detection and localisation in the crowd scenes using a block-based social force model. IET Image Process 12(1):133–137

    Article  Google Scholar 

  16. Komoto K, Nakatsuka S, Aizawa H, Kato K, Kobayashi H, Banno K (2018) A performance evaluation of defect detection by using denoising autoencoder generative adversarial networks. In: 2018 international workshop on advanced image technology (IWAIT), pp 1–4

  17. Kuang Y, Lan T, Peng X, Selasi GE, Liu Q, Zhang J (2020) Unsupervised multi-discriminator generative adversarial network for lung nodule malignancy classification. IEEE Access 8:77725–77734

    Article  Google Scholar 

  18. Kuo CFJ, Ty Fang, Lee CL, Wu HC (2019) Automated optical inspection system for surface mount device light emitting diodes. J Intell Manuf 30(2):641–655

    Article  Google Scholar 

  19. Li J, Xu X, Gao L, Wang Z, Shao J (2020) Cognitive visual anomaly detection with constrained latent representations for industrial inspection robot. Applied Soft Computing 95:106539

    Article  Google Scholar 

  20. Lian J, Jia W, Zareapoor M, Zheng Y, Luo R, Jain DK, Kumar N (2020) Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network. IEEE Transactions on Industrial Informatics 16(2):1343–1351

    Article  Google Scholar 

  21. Lin S, He Z, Sun L (2019) Defect enhancement generative adversarial network for enlarging data set of microcrack defect. IEEE Access 7:148413–148423

    Article  Google Scholar 

  22. Liu H, Zheng X, Han J, Chu Y, Tao T (2019) Survey on gan-based face hallucination with its model development. IET Image Process 13(14):2662–2672

    Article  Google Scholar 

  23. Liu J, Feng Y, Liu W, Orlando D, Li H (2020) Training data assisted anomaly detection of multi-pixel targets in hyperspectral imagery, vol 68, pp 3022–3032

  24. Liu J, Wang C, Su H, Du B, Tao D (2020) Multistage gan for fabric defect detection. IEEE Transactions on Image Processing 29:3388–3400

    Article  Google Scholar 

  25. Liu Y, Tsin Y, Lin WC (2005) The promise and perils of near-regular texture. Int J Comput Vis 62(1-2):145–159

    Article  Google Scholar 

  26. Lu Y, Liu K, Hsu C (2019) Conditional generative adversarial network for defect classification with class imbalance. In: 2019 IEEE international conference on smart manufacturing, industrial logistics engineering (SMILE), pp 146–149

  27. Lyu Y, Han Z, Zhong J, Li C, Liu Z (2020) A generic anomaly detection of catenary support components based on generative adversarial networks. IEEE Trans Instrum Meas 69(5):2439–2448

    Article  Google Scholar 

  28. Mao S, Guo J, Li Z (2019) Discriminative autoencoding framework for simple and efficient anomaly detection. IEEE Access 7:140618–140630

    Article  Google Scholar 

  29. Niu S, Lin H, Niu T, Li B, Wang X (2019) Defectgan: weakly-supervised defect detection using generative adversarial network. In: 2019 IEEE 15th international conference on automation science and engineering (CASE), pp 127–132

  30. Niu S, Li B, Wang X, Lin H (2020) Defect image sample generation with GAN for improving defect recognition. IEEE Transactions on Automation Science and Engineering 17(3):1611–1622

    Google Scholar 

  31. Oh G, Lee S (2002) Motif analysis of noisy regular textures. Pattern Recognition Letters 23 (10):1179–1190

    Article  Google Scholar 

  32. Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (gans): a survey. IEEE Access 7:36322–36333

    Article  Google Scholar 

  33. Peng Y, Ruan S, Cao G, Huang S, Kwok N, Zhou S (2019) Automated product boundary defect detection based on image moment feature anomaly. IEEE Access 7:52731–52742

    Article  Google Scholar 

  34. Quellec G, Lamard M, Cozic M, Coatrieux G, Cazuguel G (2016) Multiple-instance learning for anomaly detection in digital mammography. IEEE Trans Med Imaging 35(7):1604–1614

    Article  Google Scholar 

  35. Schlegl T, Seeböck P, Waldstein SM, Langs G, Schmidt-Erfurth U (2019) f-anogan: fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54:30–44

    Article  Google Scholar 

  36. Seeböck P, Waldstein SM, Klimscha S, Bogunovic H, Schlegl T, Gerendas BS, Donner R, Schmidt-Erfurth U, Langs G (2018) Unsupervised identification of disease marker candidates in retinal oct imaging data. IEEE Transactions on Medical Imaging 38(4):1037–1047

    Article  Google Scholar 

  37. Shen G, Pierna JAF, Baeten V, Cao Y, Han L, Yang Z (2020) Local anomaly detection and quantitative analysis of contaminants in soybean meal using near infrared imaging: the example of non-protein nitrogen. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 225:117494

    Article  Google Scholar 

  38. Skilton R, Gao Y (2020) Combining object detection with generative adversarial networks for in-component anomaly detection. Fusion Engineering and Design 159:111736

    Article  Google Scholar 

  39. Togo R, Watanabe H, Ogawa T, Haseyama M (2020) Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination. Computers in Biology and Medicine 123:103903

    Article  Google Scholar 

  40. Wang Z, She Q, Ward TE (2019) Generative adversarial networks in computer vision: a survey and taxonomy. arXiv:190601529

  41. Wu P, Liu J, Shen F (2020) A deep one-class neural network for anomalous event detection in complex scenes. IEEE Transactions on Neural Networks and Learning Systems 31(7):2609–2622

    Google Scholar 

  42. Yolcu G, Oztel I, Kazan S, Oz C, Palaniappan K, Lever TE, Bunyak F (2019) Facial expression recognition for monitoring neurological disorders based on convolutional neural network. Multimedia Tools and Applications 78(22):31581–31603

    Article  Google Scholar 

  43. Zhang H, Chen Z, Zhang C, Xi J, Le X (2019) Weld defect detection based on deep learning method. In: 2019 IEEE 15th international conference on automation science and engineering (CASE), pp 1574–1579

  44. Zhang P, He H, Gao L (2019) A nonlinear and explicit framework of supervised manifold-feature extraction for hyperspectral image classification. Neurocomputing 337:315–324

    Article  Google Scholar 

  45. Zhang Y, Dong ZY, Kong W, Meng K (2020) A composite anomaly detection system for data-driven power plant condition monitoring. IEEE Transactions on Industrial Informatics 16(7):4390–4402

    Article  Google Scholar 

Download references

Acknowledgements

This research is funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under the project number 118E607.

Author information

Authors and Affiliations

  1. Yildiz Technical University, Istanbul, Turkey

    Muhammed Ali Nur Oz & Muharrem Mercimek

  2. Canakkale Onsekiz Mart University, Canakkale, Turkey

    Ozgur Turay Kaymakci

Authors
  1. Muhammed Ali Nur Oz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muharrem Mercimek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ozgur Turay Kaymakci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammed Ali Nur Oz.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oz, M.A.N., Mercimek, M. & Kaymakci, O.T. Anomaly localization in regular textures based on deep convolutional generative adversarial networks. Appl Intell 52, 1556–1565 (2022). https://doi.org/10.1007/s10489-021-02475-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02475-3

Keywords

Search

Navigation