Power Transmission Line Fault Detection and Diagnosis Based on Artificial Intelligence Approach and its Development in UAV: A Review
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Publications [4,5,6,7,8,9,10] address techniques for carrying out preventive inspections on transmission lines using RPASs with thermal cameras, demonstrating the importance of using this technology and the impact of using aircraft to carry out inspections.
“Publications [4,5,6,7,8,9,10] address techniques for carrying out preventive inspections on transmission lines using RPASs with thermal cameras, demonstrating the importance of using this technology and the impact of using aircraft to carry out inspections.”
From Multicriteria Methodology for Prioritizing Predictive Maintenance Using RPASs (Drones) with Thermal Cameras on Transmission Lines (2025) · cites this paper as [10]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · cited 1× · see citing works
“The fact that Hough transform is used due to the design of algorithm as its end code is quick enough in order to be able to insert on the UAV development board for afterward phase [103].”
Method
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Schnorr, A., Bernardon, D., Feil, D., Fabrin, F., Konrad, C., dos Santos, L. L. C., Bitencourt, V., Fontoura, H., & Correa, C. (2025) · Sensors · doi:10.3390/s25165064 · Cited by 1 · see citing works
- Authors
- Wong, S. Y., Choe, C. W. C., Goh, H. H., Low, Y. W., Cheah, D. Y. S., & Pang, C.
- Affiliations
- Xiamen University Malaysia (MY)
- Guangxi University (CN)
- Corresponding author
- Wong, Shen Yuong
- Goh, Hui Hwang
- Venue
- Arabian Journal for Science and Engineering
- Published
- 2021-04-22
- Volume
- 46
- Issue
- 10
- Pages
- 9305-9331
- ISSN
- 1319-8025 (ISSN-L)
- 2193-567X
- 2191-4281
- OpenAlex
- W3155174489
- Cited by
- 66
- References
- 4
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 18 · 2023: 5 · 2022: 10 · 2021: 1
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Arabian Journal for Science and Engineering (publishedVersion)
- Funders
- Xiamen University (IECE-0001)
- Scientific Research and Technology Development Program of Guangxi (Guangxi University A3020051008)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
- Physical Sciences › Engineering › Mechanical Engineering › Power Line Inspection Robots
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Keywords
- fault classification, artificial intelligence, unmanned aerial vehicles, power system protection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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