Open AccessIntelligent Recognition of Transmission Line Inspection Image Based on Deep Learning
Author(s) -
Yue Qi,
Shile Mu,
Jun Wang,
Liangliang Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012056
Subject(s) - computer science , artificial intelligence , automation , deep learning , electric power transmission , fault detection and isolation , transmission line , grid , computer vision , identification (biology) , aerial image , real time computing , engineering , image (mathematics) , telecommunications , electrical engineering , geography , mechanical engineering , botany , actuator , biology , geodesy
Under the background of high-speed development of the power grid, the traditional manual detection method is low efficiency and high cost, which are not able to meet the current requirements. With the development of UAV technology, the method of using UAV to inspect transmission lines has a positive effect on improving detection efficiency and quality. In recent years, deep learning has developed rapidly, which provides a new solution for the analysis and processing of UAV aerial inspection images. This paper focuses on the deep learning algorithm to study the insulator location and fault identification in aerial photographs, which plays an important role in realizing the automation and intelligence of UAV detection.