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Self-Transfer Learning Network for Multicolor Fabric Defect Detection

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Abstract

This paper presented a self-transfer learning network (STLN) for multicolor fabric defect detection. Deep neural networks were adopted to detect defects in objects with complex backgrounds such as multicolored fabrics. It is noteworthy that the more disturbances there are on the object surface, the more difficult it is to optimize the network and the more training samples will be required. At the same time, the distinct difference in different types of multi-colored fabrics makes model difficult to apply data information. To this end, the STLN in this paper, consisting of a dataset expansion module, a dataset filtering module, a feature extraction module, a defect detection module, and a category discrimination module, used only limited raw data without the help of external data, and expanded the training set by mining the features of the raw data to better optimize the network. It is experimentally demonstrated that the STLN can achieve higher accuracy compared to deep neural networks for detecting defects of multicolor fabrics with insufficient target data.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Porebski A, Hoang VT, Vandenbroucke N, Hamad D (2018) Multi-color space local binary pattern-based feature selection for texture classification. J Electron Imag 27(1):1

    Article  Google Scholar 

  2. Tan B, Zhang Y, Pan S, Yang Q (2017) Distant domain transfer learning. Proc AAAI Conf Artif Intell. https://doi.org/10.1609/aaai.v31i1.10826

    Article  Google Scholar 

  3. Zhuang F, Cheng X, Luo P, Pan SJ, He Q (2017) Supervised representation learning with double encoding-layer autoencoder for transfer learning. ACM Trans Intell Syst Technol (TIST) 9(2):1–17

    Google Scholar 

  4. Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  5. Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. Preprint http://arxiv.org/abs/2004.10934

  6. Cai Z, Vasconcelos N (2019) Cascade r-CNN: high quality object detection and instance segmentation. IEEE Trans Pattern Anal Machine Intell 43:1483–1498

    Article  Google Scholar 

  7. Fazakis N, Karlos S, Kotsiantis S, Sgarbas K (2016) Self-trained LMT for semisupervised learning. Comput Intell Neurosci 2016:1–13

    Article  Google Scholar 

  8. Aytar Y, Zisserman A (2011) Tabula rasa: model transfer for object category detection. In: 2011 international conference on computer vision pp 2252–2259

  9. Hoffman J, Darrell T, Saenko K (2014) Continuous manifold based adaptation for evolving visual domains. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 867–874

  10. Tessler C, Givony S, Zahavy T, Mankowitz D, Mannor S (2017) A deep hierarchical approach to lifelong learning in minecraft. Proc AAAI Conf Artificial Intell. https://doi.org/10.1609/aaai.v31i1.10744

    Article  Google Scholar 

  11. Zamir AR, Wekel T, Agrawal P, Wei C, Malik J, Savarese S (2016) Generic 3d representation via pose estimation and matching. European conference on computer vision. Springer, Cham, pp 535–553

    Google Scholar 

  12. Chen J, Hu K, Yang Y, Liu Y, Xuan Q (2020) Collective transfer learning for defect prediction. Neurocomputing 416:103–116

    Article  Google Scholar 

  13. Gong Y, Shao H, Luo J, Li Z (2020) A deep transfer learning model for inclusion defect detection of aeronautics composite materials. Compos Struct 252:112681

    Article  Google Scholar 

  14. Lu X, Gong T, Zheng X (2019) Multisource compensation network for remote sensing cross-domain scene classification. IEEE Trans Geosci Remote Sens 58:2504–2515

    Article  Google Scholar 

  15. Wang Q, Gao J, Li X (2019) Weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes. IEEE Trans Image Process 28(9):4376–4386

    Article  MathSciNet  MATH  Google Scholar 

  16. Maria Carlucci F, Porzi L, Caputo B, Ricci E, Rota Bulo S (2017) Autodial: automatic domain alignment layers. In: Proceedings of the IEEE international conference on computer vision pp 5067–5075

  17. Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 4320–4328

  18. Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210

    Article  Google Scholar 

  19. Cao Z, Long M, Wang J, Jordan MI (2018) Partial transfer learning with selective adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 2724–2732

  20. Zhang J, Ding Z, Li W, Ogunbona P (2018) Importance weighted adversarial nets for partial domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 8156–8164

  21. Li G, Shao R, Wan H, Zhou M, Li M (2022) A model for surface defect detection of industrial products based on attention augmentation. Comput Intell Neurosci 2022:1–12

    Article  Google Scholar 

  22. Hoang VT, Rebhi A (2018) On comparing color spaces for fabric defect classification based on local binary patterns. In: 2018 IEEE 3rd international conference on signal and image processing (ICSIP) pp 297–300

  23. Jing JF, Ma H, Zhang HH (2019) Automatic fabric defect detection using a deep convolutional neural network. Color Technol 135(3):213–223

    Article  Google Scholar 

  24. Ouyang W, Xu B, Hou J, Yuan X (2019) Fabric defect detection using activation layer embedded convolutional neural network. IEEE Access 7:70130–70140

    Article  Google Scholar 

  25. Jeyaraj PR, Nadar ERS (2019) Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm. Int J Clothing Sci Technol 31:510–521

    Article  Google Scholar 

  26. Jing J, Zhuo D, Zhang H, Liang Y, Zheng M (2020) Fabric defect detection using the improved YOLOv3 model. J Eng Fibers Fabrics 15:155892502090826

    Article  Google Scholar 

  27. Xie H, Wu Z (2020) A robust fabric defect detection method based on improved RefineDet. Sensors 20(15):4260

    Article  Google Scholar 

  28. Weng G, Dong B, Lei Y (2021) A level set method based on additive bias correction for image segmentation. Expert Syst Appl 185:115633

    Article  Google Scholar 

  29. Hu HX, Wen G, Yu W, Cao J, Huang T (2019) Finite-time coordination behavior of multiple Euler-Lagrange systems in cooperation-competition networks. IEEE Trans Cybern 49(8):2967–2979

    Article  Google Scholar 

  30. Dai W, Qiang Y, Xue G, Yong Y (2007) Boosting for transfer learning. Machine learning, proceedings of the twenty-fourth international conference (ICML 2007), Corvallis, Oregon, USA, June 20–24, 2007. ACM

  31. Thuy MBH, Hoang VT (2019) Fusing of deep learning, transfer learning and gan for breast cancer histopathological image classification. In: International conference on computer science, Applied Mathematics and Applications, pp 255–266

  32. Zhong X, Guo S, Shan H, Gao L, Xue D, Zhao N (2018) Feature-based transfer learning based on distribution similarity. IEEE Access 6:35551–35557

    Article  Google Scholar 

  33. Bao Y, Velni JM (2020) Data-driven linear parameter-varying model identification using transfer learning. IEEE Control Syst Lett 5(5):1579–1584

    Article  MathSciNet  Google Scholar 

  34. Zhao W, Jiang W, Qiu X (2022) Big transfer learning for fine art classification. Comput Intell Neurosci 2022:1–19

    Article  Google Scholar 

  35. Davis J, Domingos P (2009) Deep transfer via second-order markov logic. In: Proceedings of the 26th annual international conference on machine learning pp 217–224

  36. Li F, Pan SJ, Jin O, Yang Q, Zhu X (2012) Cross-domain co-extraction of sentiment and topic lexicons. In: Proceedings of the 50th annual meeting of the association for computational linguistics (Vol 1: Long Papers) pp 410–419

  37. Zamir AR, Sax A, Shen W, Guibas LJ, Malik J, Savarese S (2018) Taskonomy: disentangling task transfer learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition pp 3712–3722

  38. Talo M, Baloglu UB, Yıldırım Ö, Acharya UR (2019) Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn Syst Res 54:176–188

    Article  Google Scholar 

  39. Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: International conference on machine learning pp 1180–1189. PMLR

  40. Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In: International conference on machine learning pp 2208–2217. PMLR

  41. Saito K, Watanabe K, Ushiku Y, Harada T (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3723–3732

  42. Zhu Y, Zhuang F, Wang J, Ke G, Chen J, Bian J, He Q (2020) Deep subdomain adaptation network for image classification. IEEE Transa Neural Netw Learn Syst 32(4):1713–1722

    Article  MathSciNet  Google Scholar 

  43. Zhu Y, Zhuang F, Wang J, Chen J, Shi Z, Wu W, He Q (2019) Multi-representation adaptation network for cross-domain image classification. Neural Netw 119:214–221

    Article  Google Scholar 

  44. Jang Y, Lee H, Hwang SJ, Shin J (2019) Learning what and where to transfer. In: International conference on machine learning pp 3030–3039. PMLR

  45. Tseng HY, Lee HY, Huang JB, Yang MH (2020) Cross-domain few-shot classification via learned feature-wise transformation. Preprint http://arxiv.org/abs/2001.08735

  46. Li W, Wang L, Huo J, Shi Y, Gao Y, Luo J (2020) Asymmetric distribution measure for few-shot learning. Preprint http://arxiv.org/abs/2002.00153

  47. Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition pp 10781–10790

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61473201,61772576).

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Authors and Affiliations

  1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Song Lin, Zhiyong He & Lining Sun

  2. School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215006, China

    Song Lin, Zhiyong He & Lining Sun

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  1. Song Lin
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  2. Zhiyong He
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  3. Lining Sun
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Correspondence to Zhiyong He.

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Lin, S., He, Z. & Sun, L. Self-Transfer Learning Network for Multicolor Fabric Defect Detection. Neural Process Lett 55, 4735–4756 (2023). https://doi.org/10.1007/s11063-022-11063-6

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