A point cloud segmentation method for power lines and towers based on a combination of multiscale density features and point-based deep learning

Abstract

The point segmentation of power lines and towers aims to use unmanned aerial vehicles (UAVs) for the inspection of power facilities, risk detection and modelling. Because of the unclear spatial relationship between the point clouds, the point segmentation of power lines and towers is challenging. In this paper, the power line and tower point datasets are constructed using Light Detection and Ranging (LiDAR) and a point segmentation method is proposed based on multiscale density features and a point-based deep learning network. First, the data are blocked and the neighbourhood is constructed. Second, the point clouds are downsampled to produce sparse point clouds. The point clouds before and after sampling are rotated, and their density is calculated. Next, a direct mapping method is selected to fuse the density information; a lightweight network is built to learn the features. Finally, the point clouds are segmented by concatenating the local features provided by PointCNN. The algorithm performs effectively on different types of power lines and towers. The mean interaction over union is 82.73%, and the overall accuracy can reach 91.76%. This approach can achieve the end-to-end integration of segmentation and provide theoretical support for the segmentation of large scenic point clouds.