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Deep Learning-Based Intelligent Detection Device for Insulation Pull Rod Defects

Author

Listed:
  • Hua Yu

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China)

  • Shu Niu

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China)

  • Shuai Li

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China)

  • Gang Yang

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China)

  • Xuan Wang

    (State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China)

  • Hanhua Luo

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Xianhao Fan

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Chuanyang Li

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

Abstract

This paper proposes a deep learning-based intelligent detection device for insulation pull rod defects, addressing the issues of low detection accuracy, poor timeliness of intelligent analysis, and the difficulty in preserving detection results. Firstly, by constructing the pull rod defects dataset and training the YOLOv5s network, along with commonly used object detection algorithms in industrial defect detection, the feasibility of deep learning networks for insulation pull rod defects detection is explored. Secondly, the trained model is combined to build an intelligent detection device for pull rod defects, integrating insulation pull rod image acquisition and defect detection into a unified system. The research results demonstrate that the YOLOv5s network can quickly and accurately detect pull rod defects. On the test set constructed in this paper, the detection performance metric mAP@0.5:0.95 of the trained model reached 54.7%. Specifically, the mAP@0.5 score was 86.9% at a threshold of 0.5. The detection speed FPS reached 169.5, significantly improving the detection efficiency and accuracy compared to traditional object detection algorithms. By establishing an organic connection between the image hardware acquisition device and the deep learning network, the existing problems of inefficient detection and difficult storage of detection results in pull rod defects detection methods are effectively addressed. This research provides new insights for detecting insulation pull rod defects.

Suggested Citation

  • Hua Yu & Shu Niu & Shuai Li & Gang Yang & Xuan Wang & Hanhua Luo & Xianhao Fan & Chuanyang Li, 2024. "Deep Learning-Based Intelligent Detection Device for Insulation Pull Rod Defects," Energies, MDPI, vol. 17(17), pages 1-13, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4344-:d:1467553
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