Open AccessUAV Transmission Line Inspection Object Recognition based on Mask R-CNN
Author(s) -
Yi Liu,
Haolin Huo,
Jun Fang,
Junting Mai,
Shengxiang Zhang
Publication year - 2019
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 - 10.1088/1742-6596/1345/6/062043
Subject(s) - computer science , artificial intelligence , computer vision , hough transform , electric power transmission , enhanced data rates for gsm evolution , transmission (telecommunications) , drone , line (geometry) , transmission line , computer hardware , engineering , image (mathematics) , electrical engineering , telecommunications , geometry , mathematics , biology , genetics
Currently, unmanned aerial vehicles (UAV) are applied to routine inspection tasks of power transmission devices. Deep-learning algorithm and machine vision have attracted much attention in the field of the UAV’s autonomous control as it’s an effective way to improve the efficiency of inspection. Considering the differences between the distant and close view, this paper adopts Mask R-CNN to detect various components of power transmission devices but use the methods such as processing of the edge, hole filling and Hough Transform identify the wires in distant. Some major components, such as pole, truss, cross arm, insulator string and so on, can be 100% recognized. This proposed model shows the characteristics of high recognition speed and high accuracy, which can assist UAV to inspect well.