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RBD-Net: robust breakage detection algorithm for industrial leather

Author

Listed:
  • Rong Luo

    (Qilu University of Technology (Shandong Academy of Science))

  • Ruihu Chen

    (Qilu University of Technology (Shandong Academy of Science))

  • Fengting Jia

    (Qilu University of Technology (Shandong Academy of Science))

  • Biru Lin

    (Qilu University of Technology (Shandong Academy of Science))

  • Jie Liu

    (Shandong Normal University)

  • Yafei Sun

    (Qilu University of Technology (Shandong Academy of Science))

  • Xinbo Yang

    (Shandong Normal University)

  • Weikuan Jia

    (Shandong Normal University)

Abstract

For the sake of better achieving the productivity of leather damage detection in industrial production, this paper proposes a Robust Breakage Detection Network (RBD-Net) model for leather breakage detection. The model is an optimized model of You Only Look Once (YOLO) v5 to identify and detect the degrees of damage in leather and the type of damage in leather without any damage from the image. Firstly, the backbone network is replaced by Cross Stage Partial Densely Connected Networks (CSP-DenseNet), which can better achieve the reuse of features and prevent the loss of excessive gradient flow information; secondly, Bi-directional Feature Pyramid Network (BiFPN) is added in the feature refinement stage, which can better balance the feature information at different scales; finally, the addition of the Decision Network allows for capturing not only local shapes, but also global shapes spanning a large area of the image to better identify leather breakage in the image. By conducting experiments and making comparisons, it concludes that the method performs better than existing detection models in both breakage detection, and the accuracy of cutting, etched surface, brand stigma, hole and bleaching of five types of leather is 83.9%, 80.4%, 82.4%, 95.5%, and 89.4%, respectively, which satisfies the requirement for the balance of accuracy and robustness in industrial production, and also provides some ideas for other breakage detection research.

Suggested Citation

  • Rong Luo & Ruihu Chen & Fengting Jia & Biru Lin & Jie Liu & Yafei Sun & Xinbo Yang & Weikuan Jia, 2023. "RBD-Net: robust breakage detection algorithm for industrial leather," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2783-2796, August.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:6:d:10.1007_s10845-022-01962-9
    DOI: 10.1007/s10845-022-01962-9
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    References listed on IDEAS

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    1. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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