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A dynamic inference network (DI-Net) for online fabric defect detection in smart manufacturing

Author

Listed:
  • Shuxuan Zhao

    (The University of Hong Kong)

  • Ray Y. Zhong

    (The University of Hong Kong)

  • Chuqiao Xu

    (Wuhan Textile University)

  • Junliang Wang

    (Donghua University)

  • Jie Zhang

    (Donghua University)

Abstract

Online fabric defect detection plays a critical role in the quality management of textile production. However, the high-impact and low-probability characteristics of defective samples lead to redundant design of network and hinder its real-time performance. To improve the time efficiency, this paper proposes a dynamic inference network (DI-Net) which can dynamically allocate computation resources as the complexity of image. Firstly, “AND” Gates are incorporated into the backbone to control activation of network’s function modules, allowing for dynamic adjustment of network depth. Additionally, the dynamic inference module which contains several exits with inference unit is proposed to collaborate with “AND” Gates. When sample’s confidence at specific exit satisfies the early-exit policy, the inference unit will allow it to early-exit from network and output a negative value to corresponding “AND” Gate. As a result, the output of “AND” Gate will also be negative and subsequent network will not be activated. Finally, the two-stage training strategy and exit-weighted loss function are proposed to avoid crosstalk and facilitate different exits to focus on adequate samples, enabling the efficient training of DI-Net. The experiments on the fabric dataset demonstrate that the proposed DI-Net can achieve detection precision and recall over 99% for normal samples, and approximately 95% for defective samples. Besides, its detection speed has been improved by 20%, reaching 30.1 frames per second and 20.96 m/min. This indicates that the proposed DI-Net can meet the requirements of online fabric defect detection.

Suggested Citation

  • Shuxuan Zhao & Ray Y. Zhong & Chuqiao Xu & Junliang Wang & Jie Zhang, 2025. "A dynamic inference network (DI-Net) for online fabric defect detection in smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2881-2896, April.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02387-2
    DOI: 10.1007/s10845-024-02387-2
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    References listed on IDEAS

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    1. Foivos Psarommatis & Gokan May, 2023. "A literature review and design methodology for digital twins in the era of zero defect manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 61(16), pages 5723-5743, August.
    2. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    3. Zhuxi Ma & Yibo Li & Minghui Huang & Qianbin Huang & Jie Cheng & Si Tang, 2023. "Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2431-2447, June.
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