IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i4d10.1007_s10845-024-02387-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02387-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-024-02387-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bikash Koli Dey & Hyesung Seok, 2024. "Intelligent inventory management with autonomation and service strategy," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 307-330, January.
    2. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    3. Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
    4. Bingyu Lu & Biqing Huang, 2024. "A texture-aware one-stage fabric defect detection network with adaptive feature fusion and multi-task training," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1267-1280, March.
    5. Dexing Shan & Yunzhou Zhang & Shitong Liu, 2025. "Multi-modal background-aware for defect semantic segmentation with limited data," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3313-3325, June.
    6. Jože M. Rožanec & Luka Bizjak & Elena Trajkova & Patrik Zajec & Jelle Keizer & Blaž Fortuna & Dunja Mladenić, 2024. "Active learning and novel model calibration measurements for automated visual inspection in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 1963-1984, June.
    7. Ibrahim Yousif & Liam Burns & Fadi El Kalach & Ramy Harik, 2025. "Leveraging computer vision towards high-efficiency autonomous industrial facilities," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 2983-3008, June.
    8. Chengjun Xu & Guobin Zhu, 2021. "Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 237-249, January.
    9. Nhat-To Huynh, 2024. "A multi-subpopulation genetic algorithm-based CNN approach for ceramic tile defects classification," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1781-1792, April.
    10. Diyi Zhou & Shihua Gong & Ziyue Wang & Delong Li & Huaiqing Lu, 2021. "Error analysis based on error transfer theory and compensation strategy for LED chip visual localization systems," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1345-1359, June.
    11. Zhenshuang Wu & Nian Cai & Kaiqiong Chen & Hao Xia & Shuai Zhou & Han Wang, 2024. "GAN-based statistical modeling with adaptive schemes for surface defect inspection of IC metal packages," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1811-1824, April.
    12. Yuwei Mao & Hui Lin & Christina Xuan Yu & Roger Frye & Darren Beckett & Kevin Anderson & Lars Jacquemetton & Fred Carter & Zhangyuan Gao & Wei-keng Liao & Alok N. Choudhary & Kornel Ehmann & Ankit Agr, 2023. "A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 315-329, January.
    13. Chia-Yu Hsu & Ju-Chien Chien, 2022. "Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 831-844, March.
    14. Liangrui Zhang & Xi Zhang & Mingzhou Liu, 2025. "Lightweight convolutional neural network for fast visual perception of storage location status in stereo warehouse," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3143-3163, June.
    15. Swarit Anand Singh & K. A. Desai, 2023. "Automated surface defect detection framework using machine vision and convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1995-2011, April.
    16. Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.
    17. Ruizhen Liu & Zhiyi Sun & Anhong Wang & Kai Yang & Yin Wang & Qianlai Sun, 2020. "Real-time defect detection network for polarizer based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1813-1823, December.
    18. Omid Davtalab & Ali Kazemian & Xiao Yuan & Behrokh Khoshnevis, 2022. "Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 771-784, March.
    19. Aidong Chen & Xiang Li & Hongyuan Jing & Chen Hong & Minghai Li, 2023. "Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism," Energies, MDPI, vol. 16(4), pages 1-15, February.
    20. Zhenxing Cheng & Hu Wang & Gui-Rong Liu, 2021. "Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1009-1023, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02387-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.