# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 2 Related Work
[50] used a heuristic method to reconstruct pylons for power pylons widely used in high-voltage transmission systems from an airborne Lidar point cloud, which combined both data-driven and model-driven strategies. [PERSON] and [PERSON] [51] identified the exact position of each pylon by providing accurate 2D information of all pylons as a priori knowledge. Basically, different types of a priori knowledge (e.g., horizontal coordinates, PL structure types with respect to power voltage, multiple echoes over the PLs) of the approximate positions were used to classify pylon points from ALS data. However, due to the irregular distribution of PL points, some proposed approaches have been limited in applicability to other cases [52; 53]. Overall, the most widely used strategy in the classification and extraction of power elements is to first remove ground points by filtering, so the remained non-ground points are mainly from PLs, towers, poles, and other ground objects, such as vegetation, buildings, etc. To identify the target of power elements from the filtered data, it is necessary to identify each single target from the group of non-ground points, mainly using linear features or features like large local height difference and high point density. Graphical segmentation has also been used for improvement in some methods [25; 54]. In consideration of the existing limitations, an automatic method is proposed herein to extract transmission corridor objects from UAV Lidar point clouds and reconstruct 3D models for corridor safety maintenance. First, after noise removal without ground-point filtering, a \(5\times 5\) m 2D gridding in the horizontal plane was processed to calculate grid-level features. The point cloud was then subdivided into numerous blocks of 3D grids (i.e., voxels) using an improved spatial hash structure to facilitate subsequent steps. Using features of grid points in 3D blocks, the preliminary extractions of HV PLs and pylons were carried out. The extraction results were then optimized and refined according to the connectivity between the transmission components. Next, vector curves were fitted with the extracted PL points, and towers and their insulators were reconstructed after calculating the precise center point of each HV pylon. The final reconstructions could be used to determine the areas of potential hazard points, and to provide data support for the subsequent safety analysis. The advantages of the proposed method include the following: (1) a sparse grid method is used to store and manage accumulated points in the spatial hashing matrix, which subdivides space effectively, allowing quick access; (2) there is no need for precise and complex ground-point filtering in the preprocessing, as the data after removing noise points by a statistical method are directly fed into the following steps; (3) the extractions of pylons and lines do not need any priori information; (4) the extraction precision is high in both plain and mountainous areas; (5) each pylon is reconstructed as a whole model and detailed by the modeling of insulators.