A Refined Identification Method for the Hidden Dangers of External Damage in Transmission Lines Based on the Generation of a Vanishing Point-Driven Effective Region

As the carrier of electric energy transmission, transmission lines undertake the important task of electric energy distribution and transfer. However, with the increasing frequency of construction using large machinery such as tower cranes and excavators under the transmission channels, transmission line accidents occur frequently. Therefore, this paper proposes a refined identification method for the hidden dangers of external damage in transmission lines based on the generation of effective regions driven by vanishing points. The comprehensive and accurate perception of external damage targets through the perception model of scene elements based on slicing-aided hyperinference was realized. Secondly, the accuracy and robustness of the calculation of the transmission line’s vanishing point were improved based on Canny edge detection and Hough linear detection. The effective region on the visual images was generated by combining the vanishing point and the bottom of transmission tower coordinates. Finally, the relative position relationship between areas with hidden dangers of external damage and the effective warning regions were compared, and the refined identification of hidden dangers was realized. The experimental data show that the proposed method realized a perception accuracy of 82.9% in identifying hidden dangers of external damage caused by ground- and aerial-moving targets, which shows better detection performance and practical value compared with the existing method.

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Publication Details

Journal
Processes
Published
2024-09-05
DOI
https://doi.org/10.3390/pr12091904
Citations
3
Primary Topic
Advanced Measurement and Detection Methods
Type
article
Field-Weighted Citation Impact
0.63

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article

A Refined Identification Method for the Hidden Dangers of External Damage in Transmission Lines Based on the Generation of a Vanishing Point-Driven Effective Region

Rong Jia, Jiaxun Wang, Hengrui Ma, Fuqi Ma et al.
3 citations
Processes
Advanced Measurement and Detection Methods
0.63
article

A Refined Identification Method for the Hidden Dangers of External Damage in Transmission Lines Based on the Generation of a Vanishing Point-Driven Effective Region

Rong Jia, Jiaxun Wang, Hengrui Ma, Fuqi Ma, Bo Wang, Heng Liu
article en
3 citations

Abstract

As the carrier of electric energy transmission, transmission lines undertake the important task of electric energy distribution and transfer. However, with the increasing frequency of construction using large machinery such as tower cranes and excavators under the transmission channels, transmission line accidents occur frequently. Therefore, this paper proposes a refined identification method for the hidden dangers of external damage in transmission lines based on the generation of effective regions driven by vanishing points. The comprehensive and accurate perception of external damage targets through the perception model of scene elements based on slicing-aided hyperinference was realized. Secondly, the accuracy and robustness of the calculation of the transmission line’s vanishing point were improved based on Canny edge detection and Hough linear detection. The effective region on the visual images was generated by combining the vanishing point and the bottom of transmission tower coordinates. Finally, the relative position relationship between areas with hidden dangers of external damage and the effective warning regions were compared, and the refined identification of hidden dangers was realized. The experimental data show that the proposed method realized a perception accuracy of 82.9% in identifying hidden dangers of external damage caused by ground- and aerial-moving targets, which shows better detection performance and practical value compared with the existing method.

ProcessesVol. 12(9)
State Grid Corporation of China (China) (CN), Wuhan University (CN), Xi'an University of Technology (CN)
Natural Science Basic Research Program of Shaanxi Province
Openalex Percentile: Top 32%
Advanced Measurement and Detection Methods
0.63
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