Power line corridor LiDAR point cloud segmentation using convolutional neural network

Abstract

Regular inspection is important for ensuring safe operation of the power lines. Point cloud segmentation is an efficient way to carry out these inspections. Most of the existing methods depend on priori knowledge from a paticular power line corridor, which is not applicable for other unknown power line corridors. To address this problem, we propose the first end-to-end deep learning based framework for power line corridor point cloud segmentation. Specifically, we design an effective channel presentation for Light Detection and Ranging (LiDAR) point clouds and adapt a general convolutional neural network as our basic network. To evaluate the effectiveness and efficiency of our method, we collect and label a dataset, which covers a 720,000 square meter area of power line corridors. To verify the generalization ability of our method, we also test it on KITTI dataset. Experiments shows that our method not only achieves high accuracy with fast runtime on power line corridor dataset, but also performs well on KITTI dataset.