Semantic segmentation of point clouds in transmission corridor remains a challenging task due to strong noise interference, the difficulty of accurately identifying linear structures, and severe class imbalance, all of which result in unstable segmentation performance. To address these issues, this paper proposes a robust semantic segmentation method for transmission corridor point clouds. The proposed approach integrates three key modules: k-nearest neighbor (KNN)-based noise suppression, ellipsoidal structure-aware convolution, and the Focal Loss function. These modules collaboratively enhance the model's robustness to noise, structural completeness in feature representation, and discrimination capability for minority classes. Extensive experiments were conducted on a real-world 500 kV high-voltage transmission corridor dataset and the publicly available ECLAIR dataset. Experimental results demonstrate that the proposed method achieves superior performance compared to state-of-the-art approaches such as KPConv and RandLA-Net in terms of overall accuracy (OA) and mean intersection over union (mIoU). Specifically, on the 500 kV dataset, the proposed method improves the mIoU by 9.5% over RandLA-Net and by 5.9% over KPConv. The method significantly enhances the recognition of linear components and small-category objects, exhibiting excellent accuracy, stability, and generalization capability. This work was supported by the Technical Foundation of State Grid Corporation of China under Grant (52230025000F-191-ZN).
[1] He T, Zeng Y H, Hu Z L.Research of multi-rotor UAVs detailed autonomous inspection technology of transmission lines based on route planning[J]. IEEE Access, 2019, 7: 114955-114965. DOI: 10.1109/ACCESS.2019.2935208.
[2] 芦肇基,沈艳霞,谭永强. 基于多结构融合WGAN的模糊绝缘子图像复原方法研究[J]. 电力系统保护与控制,2024,52(22):166-175. DOI:10.19783/j.cnki.pspc.240115.
[3] Xu C, Li Q W, Zhou Q K, et al.Power line-guided automatic electric transmission line inspection system[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3512118. DOI: 10.1109/TIM.2022.3169555.
[4] 麻卫峰,王成,王金亮,等. 点云数据温度变化环境下输电线形态模拟[J]. 电力系统保护与控制,2021,49(9):90-96. DOI:10.19783/j.cnki.pspc.200873.
[5] 梁磊,习晓环,王成,等. 基于B/S架构的激光雷达电力巡线可视化管理与分析系统[J]. 中国科学院大学学报,2022,39(2):201-207. DOI:10.7523/j.ucas.2020.0044.
[6] Liu X N, Shuang F, Li Y, et al.SS-IPLE: Semantic segmentation of electric power corridor scene and individual power line extraction from UAV-based lidar point cloud[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 38-50. DOI: 10.1109/JSTARS.2023.3289599.
[7] Xie Y X, Tian J J, Zhu X X.Linking points with labels in 3D: a review of point cloud semantic segmentation[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 8(4): 38-59. DOI: 10.1109/MGRS.2019.2937630.
[8] Yermo M, Laso R, Lorenzo O G, et al.Powerline detection and characterization in general-purpose airborne LiDAR surveys[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 10137-10157. DOI: 10.1109/JSTARS.2024.3396522.
[9] Chen M L, Li J Y, Pan J P, et al.Insulator extraction from UAV LiDAR point cloud based on multi-type and multi-scale feature histogram[J]. Drones, 2024, 8(6): 241. DOI: 10.3390/drones8060241.
[10] Zhang J X, Lin X G, Ning X G.SVM-based classification of segmented airborne LiDAR point clouds in urban areas[J]. Remote Sensing, 2013, 5(8): 3749-3775. DOI: 10.3390/rs5083749.
[11] Chehata N, Guo L, Mallet C.Airborne lidar feature selection for urban classification using random forests[C]//ISPRS Workshop on Laser Scanning 2009. September 1-2, 2009, Paris, France. ISPRS, 2009: 135-140.
[12] Zhuang Y, Liu Y S, He G J, et al.Contextual classification of 3D laser points with conditional random fields in urban environments[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 28 - October 2, 2015, Hamburg, Germany. IEEE, 2015: 3908-3913. DOI: 10.1109/IROS.2015.7353927.
[13] Huang J J, Shen Y Q, Wang J G, et al.Automatic pylon extraction using color-aided classification from UAV LiDAR point cloud data[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 2520611. DOI: 10.1109/TIM.2023.3293543.
[14] Tang Q Y, Zhang L T, Lan G W, et al.A classification method of point clouds of transmission line corridor based on improved random forest and multi-scale features[J]. Sensors, 2023, 23(3): 1320. DOI: 10.3390/s23031320.
[15] Liu Y X, Aleksandrov M, Zlatanova S, et al.Classification of power facility point clouds from unmanned aerial vehicles based on adaboost and topological constraints[J]. Sensors, 2019, 19(21): 4717. DOI: 10.3390/s19214717.
[16] Chen C, Jin A, Yang B S, et al.DCPLD-Net:A diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-Borne LiDAR data[J]. International Journal of Applied Earth Observation and Geoinformation,2022, 112: 102960. DOI: 10.1016/j.jag.2022.102960.
[17] 王文曦,李乐林. 深度学习在点云分类中的研究综述[J]. 计算机工程与应用,2022,58(1):26-40. DOI:10.3778/j.issn.1002-8331.2105-0200.
[18] Caros M, Just A, Segui S.Object segmentation of cluttered airborne lidar point cloudsM//Puig V, ed. Artificial Intelligence Research and Development. Amsterdam: IOS Press, 2022: 259-268. DOI: 10.3233/FAIA220347.
[19] Wang G J, Wang L N, Wu S C, et al.Semantic segmentation of transmission corridor 3D point clouds based on CA-PointNet++[J]. Electronics, 2023, 12(13): 2829. DOI: 10.3390/electronics12132829.
[20] Zhou Y Y, Feng Z Y, Chen C L, et al.Bilinear distance feature network for semantic segmentation in PowerLine corridor point clouds[J]. Sensors, 2024, 24(15): 5021. DOI: 10.3390/s24155021.
[21] Kumar V, Nandy A, Soni V, et al.Powerline extraction from aerial and mobile LiDAR data using deep learning[J]. Earth Science Informatics, 2024, 17(4): 2819-2833. DOI: 10.1007/s12145-024-01310-w.
[22] Liu T Z, Hu B Y, Gu Y F, et al.An enhanced classification method based on adaptive multi-scale fusion for long-tailed multispectral point clouds[J]. Science China Information Sciences, 2025, 68(8): 182302. DOI: 10.1007/s11432-024-4324-6.
[23] Jiang Y Z, Gu Y F, Li X.Pillar-voxel fusion network for 3D object detection in airborne hyperspectral point clouds[J]. Science China Information Sciences, 2026, 69(1): 112301. DOI: 10.1007/s11432-024-4458-0.
[24] Peng S W, Xi X H, Wang C, et al.Point-based multilevel domain adaptation for point cloud segmentation[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 6500605. DOI: 10.1109/LGRS.2020.3037702.
[25] 李建,王健,王雷,等. 双重注意力机制的电力走廊点云语义分割[J]. 测绘通报,2025(4):127-133. DOI: 10.13474/j.cnki.11-2246.2025.0421.
[26] Thomas H, Qi C R, Deschaud J E, et al.KPConv: Flexible and deformable convolution for point clouds[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27-November 2, 2019, Seoul, Korea. IEEE, 2019: 6411-6420. DOI: 10.1109/ICCV.2019.00651.
[27] Melekhov I, Umashankar A, Kim H J, et al.ECLAIR: A high-fidelity aerial LiDAR dataset for semantic segmentation[C]//2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). June 17-18, 2024, Seattle, WA, USA. IEEE, 2024: 7627-7637. DOI: 10.1109/CVPRW63382.2024.00758.
[28] Hu Q Y, Yang B, Xie L H, et al.RandLA-net: Efficient semantic segmentation of large-scale point clouds[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13-19, 2020, Seattle, WA, USA. IEEE, 2020: 11108-11117. DOI: 10.1109/CVPR42600.2020.01112.
