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A Drone Based Transmission Line Components Inspection System with Deep Learning Technique

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  • Zahid Ali Siddiqui

    (Department of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea
    Department of Electronic Engineering, NED University of Engineering & Technology, Karachi-75270, Pakistan)

  • Unsang Park

    (Department of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea)

Abstract

Defects in high voltage transmission line components such as cracked insulators, broken wires rope, and corroded power line joints, are very common due to continuous exposure of these components to harsh environmental conditions. Consequently, they pose a great threat to humans and the environment. This paper presents a real-time aerial power line inspection system that aims to detect power line components such as insulators (polymer and porcelain), splitters, damper-weights, power lines, and then analyze these transmission line components for potential defects. The proposed system employs a deep learning-based framework using Jetson TX2 embedded platform for the real-time detection and localization of these components from a live video captured by remote-controlled drone. The detected components are then analyzed using novel defect detection algorithms, presented in this paper. Results show that the proposed detection and localization system is robust against highly cluttered environment, while the proposed defect analyzer outperforms similar researches in terms of defect detection precision and recall. With the help of the proposed system automatic defect analyzing system, manual inspection time can be reduced.

Suggested Citation

  • Zahid Ali Siddiqui & Unsang Park, 2020. "A Drone Based Transmission Line Components Inspection System with Deep Learning Technique," Energies, MDPI, vol. 13(13), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3348-:d:378702
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    References listed on IDEAS

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    1. Zhenbing Zhao & Zhen Zhen & Lei Zhang & Yincheng Qi & Yinghui Kong & Ke Zhang, 2019. "Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN," Energies, MDPI, vol. 12(7), pages 1-15, March.
    2. Haiyan Cheng & Yongjie Zhai & Rui Chen & Di Wang & Ze Dong & Yutao Wang, 2019. "Self-Shattering Defect Detection of Glass Insulators Based on Spatial Features," Energies, MDPI, vol. 12(3), pages 1-14, February.
    3. Yongjie Zhai & Haiyan Cheng & Rui Chen & Qiang Yang & Xiaoxia Li, 2018. "Multi-Saliency Aggregation-Based Approach for Insulator Flashover Fault Detection Using Aerial Images," Energies, MDPI, vol. 11(2), pages 1-12, February.
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    Cited by:

    1. Jingjing Liu & Chuanyang Liu & Yiquan Wu & Huajie Xu & Zuo Sun, 2021. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images," Energies, MDPI, vol. 14(14), pages 1-19, July.
    2. Gujing Han & Min He & Mengze Gao & Jinyun Yu & Kaipei Liu & Liang Qin, 2022. "Insulator Breakage Detection Based on Improved YOLOv5," Sustainability, MDPI, vol. 14(10), pages 1-17, May.

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