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Lightweight Network-Based Surface Defect Detection Method for Steel Plates

Author

Listed:
  • Changqing Wang

    (College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
    Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
    Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China)

  • Maoxuan Sun

    (College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
    Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
    Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China)

  • Yuan Cao

    (College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
    Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
    Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China)

  • Kunyu He

    (College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
    Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
    Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China)

  • Bei Zhang

    (College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
    Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
    Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China)

  • Zhonghao Cao

    (College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
    Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
    Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China)

  • Meng Wang

    (College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
    Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
    Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China)

Abstract

This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy and speed, which improves on the classification errors and missed detections present in existing steel plate defect detection algorithms. To highlight the key elements of the desired area of surface flaws in steel plates, a void space convolutional pyramid pooling model is applied to the backbone network. This model improves the fusion of high- and low-level semantic information by designing feature pyramid networks with embedded spatial attention. According to the experimental findings, the suggested detection algorithm enhances the mapped value by about 4% once compared to the YOLOv4-Ghost detection algorithm on the homemade data set. Additionally, the real-time detection speed reaches about 103FPS, which is about 7FPS faster than the YOLOv4-Ghost detection algorithm, and the detection capability of steel surface defects is significantly enhanced to meet the needs of real-time detection of realistic scenes in the mobile terminal.

Suggested Citation

  • Changqing Wang & Maoxuan Sun & Yuan Cao & Kunyu He & Bei Zhang & Zhonghao Cao & Meng Wang, 2023. "Lightweight Network-Based Surface Defect Detection Method for Steel Plates," Sustainability, MDPI, vol. 15(4), pages 1-12, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3733-:d:1072299
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    References listed on IDEAS

    as
    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.
    2. Hail Jung & Jeongjin Rhee, 2022. "Application of YOLO and ResNet in Heat Staking Process Inspection," Sustainability, MDPI, vol. 14(23), pages 1-14, November.
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