The identification and ranging method of transmission line tree barrier based on 3D mapping of point cloud

Xianda Chen; Shuai Shao; Xiaojing Liu; Jie Yang; Nan Chen

doi:10.1117/12.2673512

14 April 2023 The identification and ranging method of transmission line tree barrier based on 3D mapping of point cloud

Xianda Chen, Shuai Shao, Xiaojing Liu, Jie Yang, Nan Chen

Author Affiliations +

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 1261307 (2023) https://doi.org/10.1117/12.2673512
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Aiming at the problem of a significant error in the current tree obstacle location of transmission lines, the point cloud 3D mapping technology is introduced to study the tree obstacle identification and location method of transmission lines. The three-dimensional point cloud information of the power transmission channel is obtained through the three-dimensional mapping of the point cloud, and the mapping from "two-dimensional picture" to "three-dimensional stereo" is realized. Using the image vision recognition algorithm in artificial intelligence, the position of the tree obstacle in the image is detected. Relying on three-dimensional mapping technology, the distance measurement model of hidden trouble of transmission line tree obstacle is established. Combined with tree obstacle distance monitoring results, the degree of hidden danger crisis is predicted, and the crisis level is quantified. By comparing the three ranging methods, it is concluded that the ranging error of the new ranging method is less than 1m, and it has higher ranging accuracy.

Citation Download Citation

Xianda Chen, Shuai Shao, Xiaojing Liu, Jie Yang, and Nan Chen "The identification and ranging method of transmission line tree barrier based on 3D mapping of point cloud", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 1261307 (14 April 2023); https://doi.org/10.1117/12.2673512

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