Extraction of power lines and pylons from LiDAR point clouds using a GCN-based method

Li, Wen and Zhang, Ziyue and Luo, Zhipeng and Xiao, Zhenlong and Wang, Cheng and Li, Jonathan (2021) Extraction of power lines and pylons from LiDAR point clouds using a GCN-based method. In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 26 Sept.-2 Oct. 2020, Waikoloa, HI, USA.

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Abstract

The routine power line inspection is critical to maintain the reliability, availability, and sustainability of electricity supply. As a key part of inspection, power lines and pylons extraction is essential for resource management and power corridor safety, especially in the mountain regions. In this paper, we proposed a deep learning based method to extract power lines and pylons using ALS point clouds. First, a structure information preserved module is designed to mine the relationship of local neighborhood points. Then, a graph convolutional network (GCN) is used as basic module to extract point features. Finally, three categories, power lines, pylons and other objects are segmented from input point clouds. In addition, we provide an effective data enhancement strategy to generate enough samples to train the proposed model. We evaluated our method using a dataset acquired by our ALS scanning system. Experimental results demonstrate that our method is superior to the state-of-the-art methods on descriptiveness and efficiency. The overall accuracy and mean time are 99.1% and 9.3 seconds, respectively.

Item Type: Conference or Workshop Item (Paper)
Keywords: Power line,pylon extraction,ALS,point cloud,graph convolutional network
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1109/IGARSS39084.2020.9323218
Depositing User: Wang, Danni
Date Deposited: 24 Mar 2021 02:05
Last Modified: 24 Mar 2021 02:05
URI: http://eprints.nottingham.ac.uk/id/eprint/64821

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