Abstract: The key components of a power transmission line include tower structure, conductor, insulator, lightning arrester, and grounding wire. The primary task of precise navigation for unmanned aerial vehicle is to construct a point cloud map of the transmission line and to segment the aforementioned components from it. To solve the problem of low accuracy in existing algorithms for segmentation of fine structures such as insulators and drainage lines in transmission lines, we propose a point cloud segmentation method for fine structures of transmission lines by improving the PointNet++ algorithm. First, the point cloud data collected by unmanned aerial vehicle airborne LiDAR on site are constructed as a point cloud segmentation dataset for power transmission lines. Then, a reasonable data augmentation method in this transmission line scenario is selected through comparative experiments and applied to this dataset. Finally, the self attention mechanism and inverted residual structure have been applied in the PointNet++ algorithm, completing the design of the semantic segmentation algorithm for key point clouds in transmission lines. Under the premise of using point cloud data as input on the entire scene transmission line site, the experimental results show that the improved PointNet++ algorithm achieves simultaneous segmentation of fine structures, wires, tower bodies, and irrelevant background points in transmission lines such as drainage lines and insulators. The average intersection over union (mIoU) reaches 80.79%, and the average F₁ score for all category segmentation reaches 88.99%.