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Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel

Author

Listed:
  • Myeongso Kim

    (Korea University)

  • Minyoung Lee

    (Korea University)

  • Minjeong An

    (Korea University)

  • Hongchul Lee

    (Korea University)

Abstract

The classification of defect types during LCD panel production is very important because it is closely related to deciding whether a defect panel is restorable. But since defect areas are very small compared to the panel area, it is hard to classify defect types by images. Therefore, we need to eliminate the background pattern of the panel, but this is not an easy task because the brightness and saturation of the background varies, even in a single image. In this paper, we propose an indicator that can distinguish between defect and background area, which is robust to brightness change and minor noises. With this indicator, we got useful defect information and images with patterns eliminated to make a more efficient defect classifier. The convolutional neural network with stacked ensemble techniques played a great role in improving defect classification performance, when various information from image preprocessing was combined.

Suggested Citation

  • Myeongso Kim & Minyoung Lee & Minjeong An & Hongchul Lee, 2020. "Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1165-1174, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01502-y
    DOI: 10.1007/s10845-019-01502-y
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    References listed on IDEAS

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    1. Kuo-Ming Tsai & Hao-Jhih Luo, 2017. "An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 473-487, February.
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    Cited by:

    1. Zhenyu Liu & Yiguo Song & Ruining Tang & Guifang Duan & Jianrong Tan, 2023. "Few-shot defect recognition of metal surfaces via attention-embedding and self-supervised learning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3507-3521, December.
    2. Aslı Çelik & Ayhan Küçükmanisa & Aydın Sümer & Aysun Taşyapı Çelebi & Oğuzhan Urhan, 2022. "A real-time defective pixel detection system for LCDs using deep learning based object detectors," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 985-994, April.
    3. Meng Xiao & Bo Yang & Shilong Wang & Yongsheng Chang & Song Li & Gang Yi, 2023. "Research on recognition methods of spot-welding surface appearances based on transfer learning and a lightweight high-precision convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2153-2170, June.

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