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Unsupervised Fabric Defect Detection Framework Based on Knowledge Distillation

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1968))

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Abstract

Fabric defect detection is a critical task in the textile industry. Efficient and accurate automated detection schemes, such as computer vision fabric quality inspection, are urgently needed. However, traditional feature-based methods are often limited and difficult to implement universal solutions in industrial scenarios due to their specificity towards certain defect types or textures. Meanwhile, machine learning methods may face difficulties in harsh industrial production environments due to insufficient data and labels. To address these issues, we propose an unsupervised defect detection framework based on knowledge distillation, which includes a visual localization module to assist with the detection task. Our approach significantly improves classification and segmentation accuracy compared to previous unsupervised methods. Besides, we perform a comprehensive set of ablation experiments to determine the optimal values of different parameters. Furthermore, our method demonstrates promising performance in both open databases and real industrial scenarios, highlighting its high practical value.

H. Liu and S. Wang—Both authors contributed equally to this research.

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Acknowledgements

This work was partly supported by the Science and Technology Innovation 2030-Key Project (Grant No. 2021ZD0201404), Key Technology Projects in Shenzhen (Grant No. JSGG20220831110203007) and Aminer. ShenZhen.ScientificSuperBrain.

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Authors and Affiliations

  1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Bei**g, China

    Haotian Liu, Siqi Wang, Chang Meng, Hengyu Zhang, **an**g **ao & **u Li

Authors
  1. Haotian Liu
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  2. Siqi Wang
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  3. Chang Meng
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  4. Hengyu Zhang
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  5. **an**g **ao
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  6. **u Li
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Corresponding author

Correspondence to **u Li .

Editor information

Editors and Affiliations

  1. Scholl of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Bei**g, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Liu, H., Wang, S., Meng, C., Zhang, H., **ao, X., Li, X. (2024). Unsupervised Fabric Defect Detection Framework Based on Knowledge Distillation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_26

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  • DOI: https://doi.org/10.1007/978-981-99-8181-6_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8180-9

  • Online ISBN: 978-981-99-8181-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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