research-article

Smart Fault Detection and Monitoring of Power Line by Drones

Authors:

HongXia NiAuthors Info & Claims

EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

Pages 501 - 505

https://doi.org/10.1145/3443467.3443805

Published: 01 February 2021 Publication History

Abstract

In this paper, we introduce a novel automatic power line inspection system based on automatic vision. This system uses UAV inspection as the main inspection method, optical images as the main data source, and deep learning as the backbone of data analysis. To facilitate the implementation of the system, we solve three major challenges of deep learning in vision-based power line inspection: (i) lack of training data; (ii) class imbalance; (iii) detection of small parts and faults. First, we create four medium-sized datasets for training component detection and classification models. Next, we apply a series of effective data enhancement techniques to balance the unbalanced classes. Finally, we propose a multi-stage component detection and classification method based on a single-shot multi-box detector and a deep residual network to detect small components and faults. The results show that the proposed system can quickly and accurately detect common failures of power line components, including the lack of a top cover, cracks on the rod and cross arm, woodpecker damage to the rod, and rot on the cross arm. Field tests show that our system has broad prospects in the Smart monitoring and inspection of power line components and the valuable addition of smart grids.

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Cited By

Anidjar OBarak ABen-Moshe BHagai ETuvyahu S(2023)A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly DetectionIEEE Access10.1109/ACCESS.2023.326270211(33336-33353)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3262702
Sandu GSamii B(2021)Physical asset management in the fourth industry revolution: mapping the literature for condition-based maintenance2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)10.1109/ISGTAsia49270.2021.9715671(1-5)Online publication date: 5-Dec-2021
https://doi.org/10.1109/ISGTAsia49270.2021.9715671

Index Terms

Smart Fault Detection and Monitoring of Power Line by Drones
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
      1. Wire routing

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Graphical abstract

Display Omitted
Highlights
- In this survey, various power line components, their faults and consequences, and sensors used to capture these faults are described.
- The challenges of vision-based methods for automatic power line inspection are identified.
- The ...

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cover image ACM Other conferences

EITCE '20: Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

November 2020

1202 pages

ISBN:9781450387811

DOI:10.1145/3443467

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 February 2021

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EITCE 2020

EITCE 2020: 2020 4th International Conference on Electronic Information Technology and Computer Engineering

November 6 - 8, 2020

Xiamen, China

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EITCE '20 Paper Acceptance Rate 214 of 441 submissions, 49%;

Overall Acceptance Rate 508 of 972 submissions, 52%

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Cited By

Anidjar OBarak ABen-Moshe BHagai ETuvyahu S(2023)A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly DetectionIEEE Access10.1109/ACCESS.2023.326270211(33336-33353)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3262702
Sandu GSamii B(2021)Physical asset management in the fourth industry revolution: mapping the literature for condition-based maintenance2021 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia)10.1109/ISGTAsia49270.2021.9715671(1-5)Online publication date: 5-Dec-2021
https://doi.org/10.1109/ISGTAsia49270.2021.9715671

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