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A novel micro-defect classification system based on attention enhancement

Author

Listed:
  • Song Lin

    (Harbin Institute of Technology)

  • Zhiyong He

    (Harbin Institute of Technology
    Soochow University)

  • Lining Sun

    (Harbin Institute of Technology)

Abstract

A surface micro-defect is characterized by a small size and a susceptibility to noise. Micro-defect detection and classification is very challenging. This paper proposes a Micro-defect classification system based on attention enhancement (MDCS) for solving the detection and classification of micro-defects. We combine defect detection with defect classification in MDSC. Micro-defects classification can be better realized based on the auxiliary task of defect detection. In this system, the aim of attention formation in bionic vision is to guide the system to focus on the target by zooming in and out on micro-defects. To avoid noise interference, an attention module based on trilinear feature confluence has been incorporated. Last but not least, the enhancement process based on the attention map improves the classification ability of micro-defects. As part of comparative experiment, we analyzed data including 19,200 fabric images and 4,800 bamboo images. In the micro-defect classification experiment based on MDCS(ResNet-50), the accuracy of fabric data and bamboo data is 88.2% and 89.4% respectively. Compared with ResNet-50, the classification accuracy (64.8%, 67.7%) is improved by 23.4% and 21.7% respectively. In the object detection experiment of micro-defects based on MDCS (ResNet-50), the accuracy of fabric data and bamboo data is 65.1% mAPs and 63.3% mAPs respectively. Compared with HRDNet, the detection accuracy (59.6% mAPs, 52.2% mAPs) is improved by 5.5% mAPs and 11.1% mAPs respectively. Experimental results demonstrate that the proposed system can counteract the interference caused by noise in small object detection, localize micro-defects accurately, and improve micro-defect classification accuracy significantly.

Suggested Citation

  • Song Lin & Zhiyong He & Lining Sun, 2024. "A novel micro-defect classification system based on attention enhancement," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 703-726, February.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-022-02064-2
    DOI: 10.1007/s10845-022-02064-2
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    References listed on IDEAS

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    1. Peng Cheng & Hai Wang & Vladimir Stojanovic & Fei Liu & Shuping He & Kaibo Shi, 2022. "Dissipativity-based finite-time asynchronous output feedback control for wind turbine system via a hidden Markov model," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(15), pages 3177-3189, November.
    2. Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
    3. Tobias Schlosser & Michael Friedrich & Frederik Beuth & Danny Kowerko, 2022. "Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1099-1123, April.
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