Intelligent Extraction Method of Railway Station Overhead Catenary Wire Features from Point Cloud Guided by Knowledge

ZHU Jun; ZHANG Chuanjun; ZHAO Jianfeng; WANG Xuezhu; FU Lin; HUANG Zhiyong; GUO Pengfei

doi:10.3969/j.issn.0258-2724.20230435

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Journal of Southwest Jiaotong University > 2025 >

ZHU Jun, ZHANG Chuanjun, ZHAO Jianfeng, WANG Xuezhu, FU Lin, HUANG Zhiyong, GUO Pengfei. Intelligent Extraction Method of Railway Station Overhead Catenary Wire Features from Point Cloud Guided by Knowledge[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230435

Citation:

ZHU Jun, ZHANG Chuanjun, ZHAO Jianfeng, WANG Xuezhu, FU Lin, HUANG Zhiyong, GUO Pengfei. Intelligent Extraction Method of Railway Station Overhead Catenary Wire Features from Point Cloud Guided by Knowledge[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230435

Citation:

ZHU Jun, ZHANG Chuanjun, ZHAO Jianfeng, WANG Xuezhu, FU Lin, HUANG Zhiyong, GUO Pengfei. Intelligent Extraction Method of Railway Station Overhead Catenary Wire Features from Point Cloud Guided by Knowledge[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230435

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Intelligent Extraction Method of Railway Station Overhead Catenary Wire Features from Point Cloud Guided by Knowledge

doi: 10.3969/j.issn.0258-2724.20230435

1.
Faculty of Geosiences and Engineering, Southwest Jiaotong University, Chengdu, 611756, China
2.
Department of Power Supply, China Railway Beijing Group Co Ltd, Beijing, 100860, China
3.
School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China
4.
Xi'an-Chengdu passenger dedicated line Shaanxi Co Ltd, 710000, China
5.
Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing, 100081, China

Received Date: 29 Aug 2023
Rev Recd Date: 26 Mar 2024

Available Online: 23 Jan 2025

Abstract

Abstract

To address the irregular noise distribution and the high difficulty of semantic segmentation in railway yard catenary point clouds, and to enhance the detection of catenary anomalies. First, the railway yard catenary scene data is analyzed, and a knowledge framework is constructed for extracting catenary wire and rail top surface point clouds. Second, a segmentation and fusion filtering method is designed, considering the spatial characteristics of railway yard point clouds. Third, strong spatial semantic constraint rules are established to guide the fine extraction of wire features. The method was tested using WHU-TLS and other railway yard point cloud datasets. An experimental platform was built for analysis. The results show that, in complex environments with partial missing of point cloud data and noise interference, the proposed method is easy to operate and highly automated. Compared to traditional methods for extracting wire features, it requires the least time and achieves an average precision of ±5 mm in extracting contact wire features within a 100 m range, effectively supporting the intelligent detection of geometric features in railway station contact networks.
- LiDAR point cloud,
- railway station catenary system,
- knowledge-guided,
- wire feature,
- intelligent extraction

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References

[1]	中华人民共和国国务院. “十四五” 现代综合交通运输体系发展规划[EB/OL]. (2021-12-09)[2023-08-01]. https://xxgk.mot.gov.cn/2020/jigou/zhghs/202201/t20220119_3637245.html.
[2]	于万聚. 高速电气化铁路接触网[M]. 成都:西南交通大学出版社,2003:323-344.
[3]	孔龙飞,韩通新. 基于激光雷达的接触网动态几何参数安全监测研究[J]. 铁道机车车辆,2019,39(4): 86-89,123. doi: 10.3969/j.issn.1008-7842.2019.04.19 KONG Longfei, HAN Tongxin. Research on safety monitoring of dynamic geometric parameters of catenary based on laser scanning radar[J]. Railway Locomotive & Car, 2019, 39(4): 86-89,123. doi: 10.3969/j.issn.1008-7842.2019.04.19
[4]	刘继冬,梁茹楠,陈交,等. 接触网承力索集中荷载测量方法[J]. 西南交通大学学报,2024,59(3): 510-518. doi: 10.3969/j.issn.0258-2724.20211092 LIU Jidong, LIANG Runan, CHEN Jiao, et al. Measurement method for concentrated load on catenary messenger wires[J]. Journal of Southwest Jiaotong University, 2024, 59(3): 510-518. doi: 10.3969/j.issn.0258-2724.20211092
[5]	周宁,支兴帅,张静,等. 电气化铁路弓网系统摩擦磨损性能研究进展[J]. 西南交通大学学报,2024,59(5): 990-1005. doi: 10.3969/j.issn.0258-2724.20220053 ZHOU Ning, ZHI Xingshuai, ZHANG Jing, et al. Friction and wear performance of pantograph-catenary system in electrified railways: state of the art[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 990-1005. doi: 10.3969/j.issn.0258-2724.20220053
[6]	DEHBI Y, HENN A, GRÖGER G, et al. Robust and fast reconstruction of complex roofs with active sampling from 3D point clouds[J]. Transactions in GIS, 2021, 25(1): 112-133. doi: 10.1111/tgis.12659
[7]	TON B, AHMED F, LINSSEN J. Semantic segmentation of terrestrial laser scans of railway catenary Arches: a use case perspective[J]. Sensors, 2022, 23(1): 222.1-222.14.
[8]	HAN F, LIANG T, REN J P, et al. Automated extraction of rail point clouds by multi-scale dimensional features from MLS data[J]. IEEE Access, 2023, 11: 32427-32436. doi: 10.1109/ACCESS.2023.3262732
[9]	梁涛,韩峰,陈国栋. 基于连续点云数据的既有铁路轨面信息快速提取算法设计[J]. 铁道科学与工程学报,2021,18(10): 2544-2551. LIANG Tao, HAN Feng, CHEN Guodong. Algorithm design for fast extraction of rail-surface information for existing railway based on continuous point cloud data[J]. Journal of Railway Science and Engineering, 2021, 18(10): 2544-2551.
[10]	SÁNCHEZ-RODRÍGUEZ A, SOILÁN M, CABALEIRO M, et al. Automated inspection of railway tunnels' power line using LiDAR point clouds[J]. Remote Sensing, 2019, 11(21): 2567.1-2567.13.
[11]	周靖松,韩志伟,杨长江. 基于三维点云的接触网几何参数检测方法[J]. 仪器仪表学报,2018,39(4): 239-246. ZHOU Jingsong, HAN Zhiwei, YANG Changjiang. Catenary geometric parameters detection method based on 3D point cloud[J]. Chinese Journal of Scientific Instrument, 2018, 39(4): 239-246.
[12]	麻卫峰,王成,王金亮,等. 激光点云输电线精细提取的残差聚类法[J]. 测绘学报,2020,49(7): 883-892. MA Weifeng, WANG Cheng, WANG Jinliang, et al. Extraction of power lines from laser point cloud based on residual clustering method[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7): 883-892.
[13]	MA T, LONG X, FENG L, et al. Visible neighborhood graph of point clouds[J]. Graphical Models, 2012, 74(4): 184-196. doi: 10.1016/j.gmod.2012.04.007
[14]	TIAN F J, JIANG Z D, JIANG G Y. DNet: dynamic neighborhood feature learning in point cloud[J]. Sensors, 2021, 21(7): 2327.1-2327.20.
[15]	赵明富,曹利波,宋涛,等. 三维点云配准中FPFH邻域半径自主选取算法[J]. 激光与光电子学进展,2021,58(6): 123-131. ZHAO Mingfu, CAO Libo, SONG Tao, et al. Independent method for selecting radius of FPFH neighborhood in 3D point cloud registration[J]. Laser & Optoelectronics Progress, 2021, 58(6): 123-131.
[16]	魏双全,房华乐,林祥国. 先验知识引导的车载激光扫描点云道路信息提取[J]. 测绘科学,2014,39(10): 81-84. WEI Shuangquan, FANG Huale, LIN Xiangguo. Road information extraction from mobile laser scanning point cloud based on priori knowledge[J]. Science of Surveying and Mapping, 2014, 39(10): 81-84.
[17]	方一鹏,宋占峰,李军. 基于TLS数据的站场线路点云提取算法[J]. 铁道科学与工程学报,2024,21(2): 545-554. FANG Yipeng, SONG Zhanfeng, LI Jun. Point cloud extraction algorithm based on TLS data in railway stations[J]. Journal of Railway Science and Engineering, 2024, 21(2): 545-554.
[18]	朱军,陈逸东,张昀昊,等. 网络环境下全景图和点云数据快速融合可视化方法[J]. 西南交通大学学报,2022,57(1): 18-27. doi: 10.3969/j.issn.0258-2724.20200360 ZHU Jun, CHEN Yidong, ZHANG Yunhao, et al. Visualization method for fast fusion of panorama and point cloud data in network environment[J]. Journal of Southwest Jiaotong University, 2022, 57(1): 18-27. doi: 10.3969/j.issn.0258-2724.20200360
[19]	CHEN X, CHEN Z, LIU G X, et al. Railway overhead contact system point cloud classification[J]. Sensors, 2021, 21(15): 4961.1-4691.22.
[20]	XU L, ZHENG S Y, NA J M, et al. A vehicle-borne mobile mapping system based framework for semantic segmentation and modeling on overhead catenary system using deep learning[J]. Remote Sensing, 2021, 13(23): 4939.1-4939.22.
[21]	郭保青,余祖俊,张楠,等. 铁路场景三维点云分割与分类识别算法[J]. 仪器仪表学报,2017,38(9): 2103-2111. doi: 10.3969/j.issn.0254-3087.2017.09.002 GUO Baoqing, YU Zujun, ZHANG Nan, et al. 3D point cloud segmentation, classification and recognition algorithm of railway scene[J]. Chinese Journal of Scientific Instrument, 2017, 38(9): 2103-2111. doi: 10.3969/j.issn.0254-3087.2017.09.002
[22]	霍佳欣,杨家志. 统计学滤波和引导滤波相结合的点云数据降噪[J]. 计算机应用与软件,2023,40(5): 248-252,287. doi: 10.3969/j.issn.1000-386x.2023.05.037 HUO Jiaxin, YANG Jiazhi. Point cloud data denoising method combining statistical filtering and guided filtering[J]. Computer Applications and Software, 2023, 40(5): 248-252,287. doi: 10.3969/j.issn.1000-386x.2023.05.037
[23]	惠振阳,李娜,程朋根,等. 基于连通性标记优化的地基LiDAR点云单木分割方法[J]. 中国激光,2023,50(6): 155-163. XI/HUI) Zhenyang, LI Na, CHENG Penggen, et al. Single tree segmentation method for terrestrial LiDAR point cloud based on connectivity marker optimization[J]. Chinese Journal of Lasers, 2023, 50(6): 155-163.
[24]	HUI Z, LI N, XIA Y, et al. Individual tree extraction from uav LIDAR point clouds based on self-adaptive mean shift segmentation[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021, 51: 25-30.
[25]	国家铁路局. 铁路线路设计规范:TB 10098—2017[S]. 北京:中国铁道出版社,2017
[26]	国家铁路局. 铁路车站及枢纽设计规范:TB 10099—2017[S]. 北京:中国铁道出版社,2017.
[27]	中国铁路总公司. 高速铁路接触网运行维修规则:TG/GD 124—2015[S]. 北京:中国铁道出版社,2015.
[28]	DONG Z, LIANG F X, YANG B S, et al. Registration of large-scale terrestrial laser scanner point clouds: a review and benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 327-342. doi: 10.1016/j.isprsjprs.2020.03.013