Powerline Detection and Characterization in General-Purpose Airborne LiDAR Surveys

Abstract

Powerline inspection and modelization using airborne light detection and ranging (LiDAR) data have been widely studied through the years. However, to the best of our knowledge, the proposed methods rely on intentional flights carried out along the high-voltage powerline. Thus, the state-of-the-art studies focus on detecting and characterizing a single powerline whose presence and location are known beforehand. We propose a method to detect and model powerlines of any voltage from airborne LiDAR point clouds not necessarily acquired for this purpose. Also, the method is suitable to be applied to those point clouds whose density is usually lower than that obtained using specific purpose flights over the powerlines. Our solution starts filtering out most of the points that do not belong to electric conductors. Then, the Hough transform is used to detect straight lines. Its output is then used to cluster the electric conductors. Also, we propose a solution to bypass a common issue regarding the nonmaxima suppression often used in object detection algorithms. Furthermore, a robust method for clustering conductors sharing the same vertical plane is presented, being able to return good results even in the absence of parts of any electrical conductor. The algorithm is tested in several datasets containing high-voltage powerlines and others, comprising mid- and low-voltage electric conductors. Finally, a study of the computational performance shows that the algorithm can efficiently take advantage of manycore systems, which is essential to determine the feasibility of our approach on massive LiDAR point clouds.

Keywords

• Airborne point cloud
• light detection and ranging (LiDAR) point clouds
• parallel computing
• powerlines