A semantic segmentation method for power transmission line point clouds based on PointNet++

Guohui Fu; Fuquan Li; Guojun Gong; Yunke Hu; Xianyong Zhang; Na Wang

doi:10.1117/12.3009543

16 October 2023 A semantic segmentation method for power transmission line point clouds based on PointNet++

Guohui Fu, Fuquan Li, Guojun Gong, Yunke Hu, Xianyong Zhang, Na Wang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031V (2023) https://doi.org/10.1117/12.3009543
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

We present a 3D point cloud semantic segmentation method for power transmission line based on PointNet++. The method aims to identify pylons, ground, vegetation, insulators, earth wires, conductors, bypass jumpers, etc. We created a dataset of 54,733,560 points, collected from real power transmission lines by airborne lidars. We modified the feature extraction network of PointNet++, the radius, and the number of samplings to fit the complicated structures of the power transmission system components. The experimental results showed accuracy improvement in all 8 categories, the highest improvement is with the insulators, by 2.73%, and the overall improvement is by 0.72%, compared to the original model.

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

Citation Download Citation

Guohui Fu, Fuquan Li, Guojun Gong, Yunke Hu, Xianyong Zhang, and Na Wang "A semantic segmentation method for power transmission line point clouds based on PointNet++", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031V (16 October 2023); https://doi.org/10.1117/12.3009543

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KEYWORDS

Dielectrics

Point clouds

Data modeling

Vegetation

Semantics

Feature extraction

3D modeling

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