Object detection in power line infrastructure: A review of the challenges and solutions
Prateek Sharma
1
,
Pratibha Sharma
1
,
Sumeet Saurav
2, 3
,
Sanjay Singh
2, 3
,
Sanjay Kumar Singh
2, 3
1
MMIT, Technical Education Department, Hathras, 204102, Uttar Pradesh, India
|
2
Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India
|
Publication type: Journal Article
Publication date: 2024-04-01
scimago Q1
wos Q1
SJR: 1.652
CiteScore: 9.5
Impact factor: 8.0
ISSN: 09521976, 18736769
Electrical and Electronic Engineering
Artificial Intelligence
Control and Systems Engineering
Abstract
Lack of proper maintenance of power line infrastructures is one of the main reasons behind power shortages and major blackouts. Current inspection methods are human-dependent, which is time-consuming and expensive. Recent progress in Unmanned Aerial Vehicles (UAVs) and digital cameras enforces the use of UAVs for power line inspection, reducing the cost and time to a great extent. Deep learning methods have recently proved their efficacy in the automatic analysis of power line data; however, they suffer from numerous challenges. Unlike generic object detection, power line inspection does not have large datasets. The data collection of power line objects is challenging compared to data collection for generic objects. As deep learning methods are data-hungry, difficulty in collecting training data raises class imbalance problems . Also, the real-time inspection of power line components demands compute-efficient deep learning methods, which is also challenging because of the high computational requirements of the generic deep learning-based object detectors. Despite being researched for decades, no object detectors can eliminate the effect of diverse challenges on the performance of deep learning methods. With these considerations, this study thoroughly reviews the existing works in the literature and the methods and approaches adopted in power line inspection to overcome these challenges. We also provide the type of faults addressed in the literature with details on the methods employed for their analysis. Finally, we conclude the review by providing insights into future research directions in power line inspection. • 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 existing open-source power line datasets are discussed and the reported results on these datasets are summarized. • The available techniques to solve the challenges of power line infrastructure are examined and valuable insights are provided for further studies.
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Metrics
35
Total citations:
35
Citations from 2024:
33
(100%)
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GOST
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Sharma P. et al. Object detection in power line infrastructure: A review of the challenges and solutions // Engineering Applications of Artificial Intelligence. 2024. Vol. 130. p. 107781.
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Sharma P., Sharma P., Saurav S., Singh S., Singh S. K. Object detection in power line infrastructure: A review of the challenges and solutions // Engineering Applications of Artificial Intelligence. 2024. Vol. 130. p. 107781.
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TY - JOUR
DO - 10.1016/j.engappai.2023.107781
UR - https://linkinghub.elsevier.com/retrieve/pii/S0952197623019656
TI - Object detection in power line infrastructure: A review of the challenges and solutions
T2 - Engineering Applications of Artificial Intelligence
AU - Sharma, Prateek
AU - Sharma, Pratibha
AU - Saurav, Sumeet
AU - Singh, Sanjay
AU - Singh, Sanjay Kumar
PY - 2024
DA - 2024/04/01
PB - Elsevier
SP - 107781
VL - 130
SN - 0952-1976
SN - 1873-6769
ER -
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BibTex (up to 50 authors)
Copy
@article{2024_Sharma,
author = {Prateek Sharma and Pratibha Sharma and Sumeet Saurav and Sanjay Singh and Sanjay Kumar Singh},
title = {Object detection in power line infrastructure: A review of the challenges and solutions},
journal = {Engineering Applications of Artificial Intelligence},
year = {2024},
volume = {130},
publisher = {Elsevier},
month = {apr},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0952197623019656},
pages = {107781},
doi = {10.1016/j.engappai.2023.107781}
}
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