Transmission Tower category Identification from Airborne LiDAR Point Clouds based on Shape Curve

Abstract

Abstract Using airborne LiDAR point cloud data to reconstruct the three-dimensional (3D) models of the transmission towers is crucial for ensuring power transmission safety. To enhance the models’ accuracy, knowing the tower categories is essential. However, there are few studies on the identification of tower categories at present. The existing studies have various shortcomings. Aiming at the problem of tower category identification in the application of airborne LiDAR point clouds in a smart grid, a transmission tower category identification method based on the shape curve is proposed. Firstly, a tower curve database is established based on relevant standards. Then, we employ three methods, namely dynamic time warping (DTW) distance, Hausdorff distance, and DTW-Hausdorff distance, to calculate the similarity indexes between the tower point cloud shape curves and those in the database. These indexes are used to determine the tower category. Finally, this method is tested using point clouds from transmission corridors. The experimental results show that the tower identification accuracy of DTW-Hausdorff distance is 94.0%. The tower category identification accuracy can reach 88.0%. The F-score used as the overall evaluation index of the tower identification effect is 90.9%. When employed as a similarity index, the tower identification effect is the best.