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author:

Chen, Qian (Chen, Qian.) [1] | Miao, Xiren (Miao, Xiren.) [2] (Scholars:缪希仁) | Jiang, Hao (Jiang, Hao.) [3] | Wang, Liangyuan (Wang, Liangyuan.) [4] | Chen, Jing (Chen, Jing.) [5] (Scholars:陈静)

Indexed by:

EI Scopus

Abstract:

Aiming at measuring tree barriers in transmission line corridors, a binocular vision ranging method is proposed to measure the distance between the transmission lines and trees. Based on the principle of binocular vision ranging, the binocular camera is calibrated using a marked checkerboard as a calibration board. Then, the SAD region matching algorithm is applied to the preprocessed images, and the disparity map is acquired. Finally, the distance between the tree and the transmission line can obtain according to the three-dimensional coordinate information of two target points. The results of the experiments show that the proposed binocular vision-based method can achieve the measurement error within ±30cm. The precision can meet the need of measuring the distance between trees and the transmission lines, which is an effective way for warning tree barriers in transmission line corridors. © 2018 IEEE.

Keyword:

Agricultural robots Binocular vision Computer vision Electric lines Forestry Robotics Stereo image processing Stereo vision Transmissions

Community:

  • [ 1 ] [Chen, Qian]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Miao, Xiren]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Jiang, Hao]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China
  • [ 4 ] [Wang, Liangyuan]State Grid Fujian Electric Power Company Limited, Fuzhou; 350003, China
  • [ 5 ] [Chen, Jing]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou; 350116, China

Reprint 's Address:

  • 陈静

    [chen, jing]college of electrical engineering and automation, fuzhou university, fuzhou; 350116, china

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Year: 2018

Page: 1514-1519

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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