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A real-time defective pixel detection system for LCDs using deep learning based object detectors

Author

Listed:
  • Aslı Çelik

    (Kocaeli University)

  • Ayhan Küçükmanisa

    (Kocaeli University)

  • Aydın Sümer

    (Kocaeli University)

  • Aysun Taşyapı Çelebi

    (Kocaeli University)

  • Oğuzhan Urhan

    (Kocaeli University)

Abstract

The presence of pixel defects on the screens of LCD-based products (TV, tablet, phone, etc.) is unacceptable given the consumer expectations. Therefore, these defects should be detected before the product reaches the user during the production stage. Visual inspections are mostly performed by human operators in the production. These inspections are error prone and not efficient in terms of consumed time. For this reason, computer visionbased approaches are started to find applications in this kind of problems. This paper presents an image acquisition system and a detailed analysis of deep learningbased object detectors for LCD pixel defect detection problem. Experimental results show that the proposed methods can be a powerful alternative to operator control by providing more efficient use of time, human, financial resources and betterquality standards in TV production industry.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:4:d:10.1007_s10845-020-01704-9
    DOI: 10.1007/s10845-020-01704-9
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    References listed on IDEAS

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    1. 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.
    2. Jueun Kwak & Ki Bum Lee & Jaeyeon Jang & Kyong Soo Chang & Chang Ouk Kim, 2019. "Automatic inspection of salt-and-pepper defects in OLED panels using image processing and control chart techniques," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1047-1055, March.
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    Cited by:

    1. Lin Huang & Weiming Deng & Chunchun Li & Tiejun Yang, 2024. "Object detection for blind inspection of industrial products based on neural architecture search," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3185-3195, October.

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