3D point cloud semantic segmentation for high-voltage transmission lines

doi:10.13474/j.cnki.11-2246.2026.0225

Abstract

Abstract: Due to the phenomenon of sample imbalance among target categories in high-voltage transmission scenarios,the intelligent inspection system for high-voltage transmission lines usually has low recognition accuracy for few-sample targets.To address this issue,this paper proposes a 3D point cloud semantic segmentation method that integrates category awareness.Firstly,an adaptive dynamic sampling strategy is introduced,which optimizes the distribution of point cloud data through density-aware region division to improve data balance.Secondly,a class-aware contextual feature enhancement module is designed,which dynamically fuses points features using class embedding information to strengthen the model's discriminative capability.Finally,a weighted loss function is constructed to alleviate learning bias caused by long-tailed data distributions.Experimental results on real-world point cloud datasets of high-voltage transmission lines demonstrate that the proposed method not only improves overall segmentation accuracy but also achieves superior recognition performance for minority classes.This study provides effective technical support for intelligent recognition of complex structural targets in power line inspection and shows promising potential for practical engineering applications.

Key words: 3D point cloud, semantic segmentation, high-voltage transmission line, feature enhancement, class imbalance

CLC Number:

GAO Shuhan, ZHOU Chao, RONG Mengqi, LIU Yangdong, LIU Hui, SHEN Hao, JIA Ran, LIU Chuanbin, ZHANG Yang, LIU Rong, SHEN Shuhan. 3D point cloud semantic segmentation for high-voltage transmission lines[J]. Bulletin of Surveying and Mapping, 2026, 0(2): 156-160.

References

[1] 邵瑰玮,刘壮,付晶,等.架空输电线路无人机巡检技术研究进展[J].高电压技术,2020,46(1):14-22.
[2] 缪希仁,刘志颖,鄢齐晨.无人机输电线路智能巡检技术综述[J].福州大学学报(自然科学版),2020,48(2):198-209.
[3] 彭向阳,钱金菊,麦晓明,等.大型无人直升机电力线路全自动巡检技术及应用[J].南方电网技术,2016,10(2):24-31,76.
[4] 吴少龙,裴嘉伟,吴宇宸,等.架空输电线路杆塔LiDAR点云塔型识别方法[J].测绘科学技术,2022,10(3):161-172.
[5] 麻卫峰.机载激光点云输电线路巡检关键技术研究[J].测绘学报,2023,52(9):1612.
[6] 张智前,叶周润,欧鑫,等.基于机载LiDAR系统的电力线点云提取方法研究[J].合肥工业大学学报(自然科学版),2023,46(8):1103-1108.
[7] 黄郑,顾徐,王红星,等.基于改进PointNet++的输电杆塔点云语义分割模型[J].中国电力,2023,56(3):77-85.
[8] 彭淑雯.输电通道机载LiDAR点云分类方法研究[D].北京:中国科学院大学,2021.
[9] 陈正宇,彭淑雯,朱号东,等.基于样本加权PointNet++的输电通道点云分类研究[J].遥感技术与应用,2021,36(6):1299-1305.
[10] 王斐然,韩庚,郭昕阳,等.激光雷达数据下架空输电线路点云场景分割及净空入侵检测[J].测绘通报,2024(5):133-137.
[11] CHARLES R Q,HAO Su,MO Kaichun,et al.PointNet:deep learning on point sets for 3D classification and segmentation[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Honolulu:IEEE,2017:77-85.
[12] QI C R,YI L,SU H,et al.Pointnet++:Deep hierarchical feature learning on point sets in a metric space[EB/OL].[2025-05-22].https://arxiv.org/abs/1706.02413.
[13] HU Qingyong,YANG Bo,XIE Linhai,et al.RandLA-net:efficient semantic segmentation of large-scale point clouds[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Seattle:IEEE,2020:11108-11117.
[14] ZHAO Hengshuang,JIANG Li,JIA Jiaya,et al.Point transformer[C]//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision (ICCV).Montreal:IEEE,2021:16239-16248.
[15] CHOY C,GWAK J,SAVARESE S.4D spatio-temporal ConvNets:minkowski convolutional neural networks[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Long Beach:IEEE,2019:3070-3079.