IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i3d10.1007_s10845-023-02270-6.html
   My bibliography  Save this article

Human–machine knowledge hybrid augmentation method for surface defect detection based few-data learning

Author

Listed:
  • Yu Gong

    (Hefei University of Technology)

  • Xiaoqiao Wang

    (Hefei University of Technology)

  • Chichun Zhou

    (Dali University
    Air-Space-Ground Integrated Intelligence and Big Data Application Engineering Research Center of Yunnan Provincial Department of Education)

  • Maogen Ge

    (Hefei University of Technology)

  • Conghu Liu

    (Suzhou University)

  • Xi Zhang

    (Hefei University of Technology)

Abstract

Visual-based defect detection is a crucial but challenging task in industrial quality control. Most mainstream methods rely on large amounts of existing or related domain data as auxiliary information. However, in actual industrial production, there are often multi-batch, low-volume manufacturing scenarios with rapidly changing task demands, making it difficult to obtain sufficient and diverse defect data. This paper proposes a parallel solution that uses a human–machine knowledge hybrid augmentation method to help the model extract unknown important features. Specifically, by incorporating experts' knowledge of abnormality to create data with rich features, positions, sizes, and backgrounds, we can quickly accumulate an amount of data from scratch and provide it to the model as prior knowledge for few-data learning. The proposed method was evaluated on the magnetic tile dataset and achieved F1-scores of 60.73%, 70.82%, 77.09%, and 82.81% when using 2, 5, 10, and 15 training images, respectively. Compared to the traditional augmentation method's F1-score of 64.59%, the proposed method achieved an 18.22% increase in the best result, demonstrating its feasibility and effectiveness in few-data industrial defect detection.

Suggested Citation

  • Yu Gong & Xiaoqiao Wang & Chichun Zhou & Maogen Ge & Conghu Liu & Xi Zhang, 2025. "Human–machine knowledge hybrid augmentation method for surface defect detection based few-data learning," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1723-1742, March.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-023-02270-6
    DOI: 10.1007/s10845-023-02270-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02270-6
    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/s10845-023-02270-6?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.

    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:3:d:10.1007_s10845-023-02270-6. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.