Automatic measurement of transmission tower height based on PointNet++ and support vector regression

Qin Jun; Chen Junyu; Su Xiao; Zhang Minghui; Zhnag Zhaoliang

doi:10.1117/12.3007482

30 August 2023 Automatic measurement of transmission tower height based on PointNet++ and support vector regression

Qin Jun, Chen Junyu, Su Xiao, Zhang Minghui, Zhnag Zhaoliang

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127971R (2023) https://doi.org/10.1117/12.3007482
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

In recent years, remote sensing, computer vision, and other methods have actively promoted the development of threedimensional (3D) reconstruction and automated measurement technologies for power grid systems. Currently, parameters such as tower height heavily rely on manual measurements, which suffer from drawbacks such as low efficiency. To address this, an automated method leveraging 3D point cloud technology for measuring transmission tower height is proposed. Firstly, the application of the PointNet++ model for segmenting 3D point clouds in transmission corridors is introduced, where the point clouds are classified into four categories: transmission lines, towers, ground, and ground wires. Subsequently, using the automatically extracted point cloud data of power towers and ground after semantic segmentation, along with relevant indicators, a prediction model for transmission tower height is constructed based on Support Vector Regression (SVR). Finally, the model is tested on a collection of 3D point cloud samples from transmission corridors. Experimental results demonstrate that the proposed automated method for calculating tower heights achieves a Mean Absolute Percentage Error (MAPE) of 5.86% and enables accurate estimation of transmission tower heights.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qin Jun, Chen Junyu, Su Xiao, Zhang Minghui, and Zhnag Zhaoliang "Automatic measurement of transmission tower height based on PointNet++ and support vector regression", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127971R (30 August 2023); https://doi.org/10.1117/12.3007482

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KEYWORDS

Point clouds

3D modeling

Data modeling

Data transmission

Image segmentation

Semantics

Deep learning

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