# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 6 Conclusions  
This paper addressed the issue of automatic object detection and reconstruction from UAV airborne point cloud data in power transmission corridors, including the extraction of HV towers and overhead transmission lines. Additionally, accurate localization and reconstruction of pylons was explored.  
An automatic method of power transmission object extraction was proposed, based on grid structures. The local collinearity of PLs in horizontal projection over their adjacent grid cells and parallel configuration among adjacent conductors were analyzed as features for segmentation. To roughly extract overhead PLs based on 2D gridding cells, the local line segments were clustered and merged. In addition, the features of large height difference, local maximum height, and continuous height distribution of HV tower were analyzed in order to detect and localize the pylons. The optimized extraction algorithm was then tested in the experiments. Results indicated that the proposed automatic power transmission object extraction method achieved high efficiency and nearly 97% accuracy in complex environments. The advantages of the proposed method are as follows:  
* The method utilizes a high volume of Lidar point cloud data with high density, and the spatial hashing matrix is utilized to store the grid data dynamically, which is beneficial for rapid access to the data.
* The integrated analysis of PLs and pylons to refine the extraction results has been proven to produce a great improvement of accuracy.
* Ground point filtering was not required, so the performance of the method does not depend on any ground point filtering algorithm. Additionally, it has a strong adaptability to ALS data with undulating terrain, and areas with clumped trees, single tall trees, and tall signal poles.
* The coordinates of the HV towers are localized accurately and robustly by extracting reliable center points of the cross-section layers in the height direction.  
Figure 22: Height clustering in pylon segmentation: (**a**) grids of large height difference; (**b**) a close-up view of a tower; (**c,d**) removal of points adjacent to a pylon.  
The proposed method automatically extracted and reconstructed transmission corridor objects (PLs and pylons) in the studied area using UAV Lidar data. It was efficient in terms of speed, quality, and scalability. The algorithm has a few requirements for the data, such as a moderate gap between points. When the data gap in the main direction of the PLs is bigger than 4 m, the results will be affected. The processing efficiency of the algorithm can be further improved, and other surrounding objects (buildings, trees, railways, other poles, etc.) and small components (nests) may be also extracted in the future. In addition, physical analysis for structural health monitoring will be investigated [74], taking the stability of the ground base into consideration, such as to detect the inclination or collapse of towers, etc. This could be further integrated via structure from motion (SfM) [75].  
Conceptualization, R.Z., Y.L.; Data curation, R.Z. and Y.L.; Formal analysis, R.Z. and F.L.; Investigation, F.L.; Methodology, R.Z., B.Y. and F.L.; Project administration, B.Y.; Software, R.Z. and Y.L.; Supervision, B.Y.; Validation, W.X.; Visualization, R.Z.; Writing--Original Draft, R.Z.; Writing--Review & Editing, R.Z., W.X.; Funding Acquisition, B.Y.; Resources, Z.W.  
This research was jointly funded by the National Science Fund for Distinguished Young Scholars of China, grant number 41725005; the National Key Research and Development Program of China, grant number 2016 YFB0103501; the Key Program of the National Natural Science Foundation of China, grant number 41531177.  
We would like to thank the anonymous reviewers for their valuable feedback. And thanks to the editors of MDPI for the language help.  
The authors declare no conflict of interest.