Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
Title Development of Deep Learning-based Automatic Camera Gimbal System for Drone Inspection of Transmission Lines
Authors 류서현(Seo-Hyeon Ryu) ; 박준영(Joon-Young Park) ; 김석태(Seok-Tae Kim) ; 김태원(Tae-Won Kim) ; 고병성(Byung-Sung Ko) ; 우정욱(Jung-Wook Woo)
DOI https://doi.org/10.5370/KIEE.2020.70.1.121
Page pp.121-129
ISSN 1975-8359
Keywords Drone; Automatic Camera Gimbal; Transmission Line Inspection; Deep Learning; Object Detection; AI
Abstract In this paper, a new automatic camera gimbal system was developed to efficiently inspect transmission lines using a drone. As drone technology has advanced tremendously over the past few years, drones are being used to replace dangerous tasks in the electric power industry. Especially, Korea Electric Power Corporation has used the automatic drone inspection system developed for power transmission lines, but while this drone system flies on autopilot, its camera gimbal is still controlled manually. Moreover, the camera gimbal control in the field was often interrupted by electromagnetic interference from ultra-high voltage power lines. To overcome this problem, we developed the new camera gimbal system that can automatically shoot power facilities on the basis of Deep Learning. Its control algorithms mainly consists of a photographing algorithm for a steel tower, a photographing algorithm for power conductors, and an automatic drone landing algorithm. This fully automated system is expected to greatly increase the task efficiency and accuracy for transmission line inspection without any kinds control efforts.