A Drone Based Transmission Line Components Inspection System with Deep Learning Technique
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....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/13/3348 |
| _version_ | 1797563673591414784 |
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| author | Zahid Ali Siddiqui Unsang Park |
| author_facet | Zahid Ali Siddiqui Unsang Park |
| author_sort | Zahid Ali Siddiqui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:46:48Z |
| format | Article |
| id | doaj.art-c483b12efb964918b2744ea42e2e027c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:46:48Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c483b12efb964918b2744ea42e2e027c2023-11-20T05:29:37ZengMDPI AGEnergies1996-10732020-06-011313334810.3390/en13133348A Drone Based Transmission Line Components Inspection System with Deep Learning TechniqueZahid Ali Siddiqui0Unsang Park1Department of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, KoreaDepartment of Computer Science & Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, KoreaDefects 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.https://www.mdpi.com/1996-1073/13/13/3348deep learningConvolutional Neural NetworksHV transmission line componentsdigital image processingdefect analysiscorrosion |
| spellingShingle | Zahid Ali Siddiqui Unsang Park A Drone Based Transmission Line Components Inspection System with Deep Learning Technique Energies deep learning Convolutional Neural Networks HV transmission line components digital image processing defect analysis corrosion |
| title | A Drone Based Transmission Line Components Inspection System with Deep Learning Technique |
| title_full | A Drone Based Transmission Line Components Inspection System with Deep Learning Technique |
| title_fullStr | A Drone Based Transmission Line Components Inspection System with Deep Learning Technique |
| title_full_unstemmed | A Drone Based Transmission Line Components Inspection System with Deep Learning Technique |
| title_short | A Drone Based Transmission Line Components Inspection System with Deep Learning Technique |
| title_sort | drone based transmission line components inspection system with deep learning technique |
| topic | deep learning Convolutional Neural Networks HV transmission line components digital image processing defect analysis corrosion |
| url | https://www.mdpi.com/1996-1073/13/13/3348 |
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