IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i3d10.1007_s10845-023-02105-4.html
   My bibliography  Save this article

A texture-aware one-stage fabric defect detection network with adaptive feature fusion and multi-task training

Author

Listed:
  • Bingyu Lu

    (Beijing National Research Center for Information Science and Technology)

  • Biqing Huang

    (Beijing National Research Center for Information Science and Technology)

Abstract

Fabric defect detection is an indispensable process to guarantee product quality in industrial production. With the proposal of industry 4.0, manufacturing enterprises have been endeavoring to develop automatic fabric defect detection systems to overcome the shortcomings of traditional manual defect detection, and many researchers have been devoting themselves to designing fabric defect detection methods with high effectiveness and strong anti-noise capacity. In the paper, we notice that the perception of the normal fabric textures is beneficial to detecting fabric defects, which is ignored in previous researches for classic one-stage detection network. Based on this finding, we propose a texture-aware one-stage fabric defect detection network (TADet). First, an adaptive feature fusion module is designed to merge and enhance multi-resolution features, where the attention mechanism is employed to make the feature fusion process adaptive to the input fabric images with different textures. Second, a multi-task defect detection head is proposed for forcing the network to distinguish the defect regions and texture regions while classifying and locating defects simultaneously in the training phase. With the defect-texture distinguishing tasks, the network is able to consider the fabric textures explicitly when detecting defects. The experimental results show that the proposed TADet outperforms other two-stage models by 3.7% and one-stage detection models by 9.3% on mAP. Besides, further experiments verify the high efficiency and strong noise-robustness of the proposed TADet, which shows its potential for industrial applications.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:3:d:10.1007_s10845-023-02105-4
    DOI: 10.1007/s10845-023-02105-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02105-4
    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/s10845-023-02105-4?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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lei Zhou & Bingya Ma & Yanyan Dong & Zhewen Yin & Fan Lu, 2025. "DCFE-YOLO: A novel fabric defect detection method," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-21, January.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Feng Huang & Ben-wu Wang & Qi-peng Li & Jun Zou, 2023. "Texture surface defect detection of plastic relays with an enhanced feature pyramid network," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1409-1425, March.
    19. Nhat-To Huynh & Duong-Dong Ho & Hong-Nguyen Nguyen, 2023. "An Approach for Designing an Optimal CNN Model Based on Auto-Tuning GA with 2D Chromosome for Defect Detection and Classification," Sustainability, MDPI, vol. 15(6), pages 1-14, March.

    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:35:y:2024:i:3:d:10.1007_s10845-023-02105-4. 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.