Urban power line extraction from mobile LiDAR point clouds based on local line-plane seperation model and global refinement

Abstract Power line extraction from three-dimensional (3D) point clouds is a key step for power inspection. However, the power line is intertwined and covered with trees and other objects in the urban area, which brings a tremendous challenge to its extraction. This article proposed a local-to-global method to extract power lines from mobile Light Detection and Ranging point clouds based on multiple characteristics. Firstly, initial point clouds clusters are selected through data preprocessing. Secondly, given the linearity and independence of the power line, we innovatively design a local line-plane separation modeling to divide the point clouds into high-confidence power line points, non-power line points, and unconfirmed points. Then, a novel global energy function is designed for the graph cut model according to the long linear and short radius characteristics of the power line. The proposed method has been tested on Toronto 3D and WuHan Avenue datasets. The average Precision , Recall , and F1score can reach up to 0.92, 0.73, and 0.79 respectively, which is superior to state-of-the-art approaches. The result demonstrates the efficacy and robustness of our proposed approach in extracting power lines within urban environments using a mobile laser scanning system.