Weakly Supervised Complex Texture Defect Detection Based on Nested U-Net Architecture

1. Qin, M., Zhang, W., Ji, X., Wu, X., Qin, L.: Application research of composite materials in aerospace and rail transit in China. Hot Work. Technol. (2024). https://doi.org/10.14158/j.cnki.1001-3814.2023096610.14158/j.cnki.1001-3814.20230966 ↗

2. Choi, D.C., Jeon, Y.J., Kim, S.H., Moon, S., Yun, J.P., Kim, S.W.: Detection of pinholes in steel slabs using Gabor filter combination and morphological features. ISIJ Int. 57(6), 1045–1053 (2017). https://doi.org/10.2355/isij-international.ISIJINT-2016-16010.2355/isij-international.ISIJINT-2016-160 ↗

3. Ma, Y., Li, Q., Zhou, Y., He, F., Xi, S.: A surface defects inspection method based on multidirectional gray-level fluctuation. Int. J. Adv. Rob. Syst. 14(3), 1729881417703114 (2017). https://doi.org/10.1177/172988141770311410.1177/1729881417703114 ↗

4. He, G., Zhu, F., Zou, H., Yao, Y.: Chip surface defect detection algorithm based on improved Frangi filter. Comput. Integr. Manuf. Syst. (2023). https://doi.org/10.13196/j.cims.2023.039710.13196/j.cims.2023.0397 ↗

5. Liu Y. (2021). Research and Application of on-line detection algorithm for surface defects of cold rolled strip steel. Dissertation, University of Science and Technology Beijing. https://doi.org/10.26945/d.cnki.gbjku.2020.000367.10.26945/d.cnki.gbjku.2020.000367 ↗

6. Ameri, R., Hsu, C.C., Band, S.S.: A systematic review of deep learning approaches for surface defect detection in industrial applications. Eng. Appl. Artif. Intell. 130, 107717 (2024). https://doi.org/10.1016/j.engappai.2023.10771710.1016/j.engappai.2023.107717 ↗

7. Zhang, H., Pan, R., Chang, F., He, L., Dong, Z., Yang, J.: Zero-DD: zero-sample defect detection for industrial products. Comput. Electr. Eng. 105, 108516 (2023). https://doi.org/10.1016/j.compeleceng.2022.10851610.1016/j.compeleceng.2022.108516 ↗

8. Luo, D., Cai, Y., Yang, Z., Zhang, Z., Zhou, Y., Bai, X.: Survey on industrial defect detection with deep learning. J. Sci. Sin. Inf 52(6), 1002–1039 (2022). https://doi.org/10.1360/SSI-2021-033610.1360/SSI-2021-0336 ↗

9. Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., Shao, L.: Surface defect detection methods for industrial products: a review. Appl. Sci. 11(16), 7657 (2021). https://doi.org/10.3390/app1116765710.3390/app11167657 ↗

10. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020). https://doi.org/10.1145/342262210.1145/3422622 ↗

11. Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016). You only look once: unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788). https://doi.org/10.48550/arXiv.1506.02640.10.48550/arXiv.1506.02640 ↗

12. He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2961–2969). https://doi.org/10.48550/arXiv.1703.06870.10.48550/arXiv.1703.06870 ↗

13. Ronneberger, O., Fischer, P., Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation. In Medical image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, part III 18 (pp. 234–241). Springer International Publishing. https://doi.org/10.1007/978-3-319-24574-4_28.10.1007/978-3-319-24574-4_28 ↗

14. Xiao, L., Wu, B., Hu, Y.: Missing small fastener detection using deep learning. IEEE Trans. Instrum. Meas. 70, 1–9 (2020). https://doi.org/10.1109/TIM.2020.302350910.1109/TIM.2020.3023509 ↗

15. Xiao, L., Huang, T., Wu, B., Hu, Y., & Zhou, J. (2019, August). Surface defect detection using hierarchical features. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) (pp. 1592–1596). IEEE. https://doi.org/10.1109/COASE.2019.8843235.10.1109/COASE.2019.8843235 ↗

16. Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile. Vis. Comput. 36(1), 85–96 (2020). https://doi.org/10.1007/s00371-018-1588-510.1007/s00371-018-1588-5 ↗

17. Jing, J., Wang, Z., Rätsch, M., Zhang, H.: Mobile-Unet: An efficient convolutional neural network for fabric defect detection. Text. Res. J. 92(1–2), 30–42 (2022). https://doi.org/10.1177/004051752092860410.1177/0040517520928604 ↗

18. Zhou, G., Zhang, Z., Yin, W., Chen, H., Wang, L., Wang, D., Ma, H.: Surface defect detection of CFRP materials based on infrared thermography and attention U-net algorithm. Nondestruct. Test. Eval. 39(2), 238–257 (2024). https://doi.org/10.1080/10589759.2023.219195410.1080/10589759.2023.2191954 ↗

19. Liu, R., Li, M., Liang, Y.: Fabric defect detection method based on improved U-net. J. Phys. Conf. Series 1948, 012160 (2021)10.1088/1742-6596/2033/1/012160 ↗

20. He, G., Zhu, J.: WT-U-Net++: surface defect detection network based on wavelet transform. J. Comput. Appl. 43(10), 3260 (2023)

21. Xiao, L., Wu, B., Hu, Y., Liu, J.: A hierarchical features-based model for freight train defect inspection. IEEE Sens. J. 20(5), 2671–2678 (2019). https://doi.org/10.1109/JSEN.2019.295412410.1109/JSEN.2019.2954124 ↗

22. Xiao, L., Wu, B., Hu, Y.: Surface defect detection using image pyramid. IEEE Sens. J. 20(13), 7181–7188 (2020). https://doi.org/10.1109/JSEN.2020.297736610.1109/JSEN.2020.2977366 ↗

23. Xiao, L., Wu, B., Hu, Y.: OSED: object-specific edge detection. J. Vis. Commun. Image Represent. 72, 102918 (2020). https://doi.org/10.1016/j.jvcir.2020.10291810.1016/j.jvcir.2020.102918 ↗

24. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., Liang, J. (2018). Unet++: a nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4 (pp. 3–11). Springer International Publishing. https://doi.org/10.1007/978-3-030-00889-5_1.10.1007/978-3-030-00889-5_1 ↗

25. Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput.-Aided Civil Infrastruct. Eng. 32(5), 361–378 (2017). https://doi.org/10.1111/mice.1226310.1111/mice.12263 ↗

26. Weimer, D., Scholz-Reiter, B., Shpitalni, M.: Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. 65(1), 417–420 (2016). https://doi.org/10.1016/j.cirp.2016.04.07210.1016/j.cirp.2016.04.072 ↗

27. Xu, C., Li, J., Li, X.: A highly efficient and lightweight detection method for steel surface defect. J. Nondestr. Eval. 43(3), 71 (2024). https://doi.org/10.1007/s10921-024-01084-710.1007/s10921-024-01084-7 ↗

28. Tao, X., Zhang, D., Ma, W., Liu, X., Xu, D.: Automatic metallic surface defect detection and recognition with convolutional neural networks. Appl. Sci. 8(9), 1575 (2018). https://doi.org/10.3390/app809157510.3390/app8091575 ↗

29. Zhang, G., Cui, K., Hung, T. Y., Lu, S. (2021). Defect-GAN: high-fidelity defect synthesis for automated defect inspection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2524–2534). https://doi.org/10.48550/arXiv.2103.15158.10.48550/arXiv.2103.15158 ↗

30. Gan, J., Wang, X.: Surface defect detection of fiber placement based on virtual sample generation. Acta Aeronautica et Astronautica Sinica 1, 260–274 (2024). https://doi.org/10.7527/S1000-6893.2023.2862410.7527/S1000-6893.2023.28624 ↗

31. Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018). https://doi.org/10.3390/s1804106410.3390/s18041064 ↗

32. Zavrtanik, V., Kristan, M., Skočaj, D. (2021). Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 8330–8339). https://doi.org/10.48550/arXiv.2108.07610.10.48550/arXiv.2108.07610 ↗

33. Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International Conference on Information Processing in Medical Imaging (pp. 146–157). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-59050-9_12.10.1007/978-3-319-59050-9_12 ↗

34. Schlegl, T., Seeböck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30–44 (2019). https://doi.org/10.1016/j.media.2019.01.01010.1016/j.media.2019.01.010 ↗

35. Akcay, S., Atapour-Abarghouei, A., Breckon, T. P. (2019). Ganomaly: Semi-supervised anomaly detection via adversarial training. In Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14 (pp. 622–637). Springer International Publishing. https://doi.org/10.1007/978-3-030-20893-6_39.10.1007/978-3-030-20893-6_39 ↗

36. Hu, Y., He, N., Xie, L., Chen, D., Gao, C., Ding, H.: Improved automatic defect detection from X-ray scans for aluminum conductor composite core wire based on modified Skip-GANomaly. NDT and E Int. 143, 103050 (2024). https://doi.org/10.1016/j.ndteint.2024.10305010.1016/j.ndteint.2024.103050 ↗

37. Zhao, Z., Li, B., Dong, R., Zhao, P. (2018). A surface defect detection method based on positive samples. In PRICAI 2018: Trends in Artificial Intelligence: 15th Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, August 28–31, 2018, Proceedings, Part II 15 (pp. 473–481). Springer International Publishing. https://doi.org/10.1007/978-3-319-97310-4_54.10.1007/978-3-319-97310-4_54 ↗

38. Huang, Y., Xiong, W., Zhang, H., Zhang, W.: Yarn-dyed fabric defect detection based on U-shaped swin transformer auto-encoder. Laser Optoelectron. Prog. 12, 293–300 (2023). https://doi.org/10.3788/LOP22069110.3788/LOP220691 ↗

39. Lin, D., Li, Y., Prasad, S., Nwe, T. L., Dong, S., Oo, Z. M. (2020). CAM-UNET: class activation MAP guided UNET with feedback refinement for defect segmentation. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 2131–2135). IEEE. https://doi.org/10.1109/ICIP40778.2020.9190900.10.1109/ICIP40778.2020.9190900 ↗

40. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2921–2929). https://doi.org/10.1109/CVPR.2016.319.10.1109/CVPR.2016.319 ↗

41. Kimura, D., Chaudhury, S., Narita, M., Munawar, A., Tachibana, R. (2020). Adversarial discriminative attention for robust anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2172–2181). https://doi.org/10.1109/WACV45572.2020.9093428.10.1109/WACV45572.2020.9093428 ↗

42. Ristea, N. C., Madan, N., Ionescu, R. T., Nasrollahi, K., Khan, F. S., Moeslund, T. B., Shah, M. (2022). Self-supervised predictive convolutional attentive block for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13576–13586). https://doi.org/10.1109/CVPR52688.2022.01321.10.1109/CVPR52688.2022.01321 ↗

43. Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A. (2014). Describing textures in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3606–3613). https://doi.org/10.1109/CVPR.2014.461.10.1109/CVPR.2014.461 ↗

44. Hu, J., Shen, L., Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7132–7141). https://doi.org/10.1109/CVPR.2018.00745.10.1109/CVPR.2018.00745 ↗

45. Liu, T., Luo, R., Xu, L., Feng, D., Cao, L., Liu, S., Guo, J.: Spatial channel attention for deep convolutional neural networks. Mathematics 10(10), 1750 (2022). https://doi.org/10.1109/CVPR42600.2020.0115510.1109/CVPR42600.2020.01155 ↗

46. Lin, T. Y., Goyal, P., Girshick, R., He, K., Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on computer Vision (pp. 2980–2988). https://doi.org/10.48550/arXiv.1708.02002.10.48550/arXiv.1708.02002 ↗

47. 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/CVF Conference on Computer Vision and Pattern Recognition (pp. 9592–9600). https://doi.org/10.1109/CVPR.2019.00982.10.1109/CVPR.2019.00982 ↗

48. Wieler, M., Hahn, T. (2007, September). Weakly supervised learning for industrial optical inspection. In DAGM symposium in (Vol. 6, p. 11). https://conferences.mpi-inf.mpg.de/dagm/2007/prizes.html#industry.

49. Li, X., Yang, L., Sun, C., Li, X., Liu, Y.: High-pressure turbine blade defect detection technology based on micro-focus industrial CT. Foundry 72(7), 904–908 (2023). https://doi.org/10.3969/j.issn.1001-4977.2023.07.01910.3969/j.issn.1001-4977.2023.07.019 ↗

50. Dong, F., Wang, C., Fan, L., Zhao, F., Zhang, X., Zheng, S.: Influence of X-ray CT imaging detection method on testing results of delamination defects in composite material. Compos. Sci. Eng. 3, 86–91 (2019). https://doi.org/10.3969/j.issn.1003-0999.2019.03.01610.3969/j.issn.1003-0999.2019.03.016 ↗

51. Ge, T.A., Xinyu, Z.H., Yuxiang, W.A., Peng, F.E., Biao, W.E.: Applications of industrial computed tomography technology in the geosciences. CT Theory Appl. 33(1), 119–134 (2024). https://doi.org/10.15953/j.ctta.2023.09110.15953/j.ctta.2023.091 ↗

52. Defard, T., Setkov, A., Loesch, A., Audigier, R. (2021). Padim: a patch distribution modeling framework for anomaly detection and localization. In International Conference on Pattern Recognition (pp. 475–489). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-68799-1_35.10.1007/978-3-030-68799-1_35 ↗

53. Deng, H., Li, X. (2022). Anomaly detection via reverse distillation from one-class embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9737–9746). https://doi.org/10.48550/arXiv.2201.10703.10.48550/arXiv.2201.10703 ↗

54. 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). https://doi.org/10.1016/j.engappai.2023.10583510.1016/j.engappai.2023.105835 ↗

55. Xu, H., Zhang, Y., Chen, X., Jing, C., Sun, L., Huang, Y., Ding, X.: Afsc: adaptive fourier space compression for anomaly detection. IEEE Trans. Industr. Inf. (2024). https://doi.org/10.1109/TII.2024.342333110.1109/TII.2024.3423331 ↗