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

Hua Yu, Shu Niu, Shuai Li, Gang Yang, Xuan Wang, Hanhua Luo, Xianhao Fan () and Chuanyang Li
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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

Energies, 2024, vol. 17, issue 17, 1-13

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.

Keywords: insulation pull rod defects; deep learning network; object detection; intelligent insulation pull rod defects detection device (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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