Open AccessIntrusion Object Detection on Transmission Corridors Based on Deep Learning
Author(s) -
Xiaochen Mu,
Xuekun Zhou,
Changyong Li,
Wenjun He,
Xin Wei
Publication year - 2020
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 - 10.1088/1742-6596/1601/2/022051
Subject(s) - computer science , reliability (semiconductor) , transmission (telecommunications) , grid , transmission line , electric power transmission , power transmission , identification (biology) , intrusion , scope (computer science) , intrusion detection system , power grid , power (physics) , computer security , reliability engineering , real time computing , telecommunications , engineering , electrical engineering , geography , physics , botany , geodesy , geochemistry , quantum mechanics , geology , biology , programming language
With the construction and development of Chinese power grid, the scale of the power grid continues to expand, the transmission corridors environment is becoming more and more complex, the possibility of external damage caused by intrusion objects of the transmission line is constantly increasing, which seriously threatens the normal operation of the power grid and the reliability of power supply. In order to solve the above problems, it is particularly important to study a detection method that can replace manual identification. That method helps the field personnel to timely detect the intrusion objects within the monitoring scope of the transmission line, so as to improve the efficiency and accuracy of transmission channel inspection.