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

Zahid Ali Siddiqui and Unsang Park
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Zahid Ali Siddiqui: Department of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea
Unsang Park: Department of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea

Energies, 2020, vol. 13, issue 13, 1-24

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.

Keywords: deep learning; Convolutional Neural Networks; HV transmission line components; digital image processing; defect analysis; corrosion; power line detection; electrical safety (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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