高电压技术2024，Vol.50Issue(5)：1943-1953,中插9,12.DOI:10.13336/j.1003-6520.hve.20230996
基于改进PointNet++的输电线路关键部位点云语义分割研究
Research on Semantic Segmentation of Point Cloud for Key Parts of Transmission Lines Based on Improved PointNet++
摘要
Abstract
The key components of a power transmission line include tower structure,conductor,insulator,lightning ar-rester,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 Point-Net++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 atten-tion 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++algo-rithm 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%.关键词
点云深度学习/点云语义分割/数据增强/自注意力/倒置残差Key words
point cloud deep learning/semantic segmentation of point clouds/data augmentation/self attention/inverted residual引用本文复制引用
杨文杰,裴少通,刘云鹏,胡晨龙,杨瑞,张行远..基于改进PointNet++的输电线路关键部位点云语义分割研究[J].高电压技术,2024,50(5):1943-1953,中插9,12.基金项目
中央高校基本科研业务费基金(2020MS093).Project supported by Fundamental Research Funds for the Central Universities(2020MS093). （2020MS093）
