{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T17:55:29Z","timestamp":1768413329757,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771402, 41804009, 42071410"],"award-info":[{"award-number":["41771402, 41804009, 42071410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2017YFB0502700"],"award-info":[{"award-number":["2017YFB0502700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the railway overhead contact system (OCS) is the key component along the high-speed railway, it is crucial to detect the quality of the OCS. Compared with conventional manual OCS detection, the vehicle-mounted Light Detection and Ranging (LiDAR) technology has advantages such as high efficiency and precision, which can solve the problems of OCS detection difficulty, low efficiency, and high risk. Aiming at the contact cables, return current cables, and catenary cables in the railway vehicle-mounted LiDAR OCS point cloud, this paper used a scale adaptive feature classification algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm considering OCS characteristics to classify the OCS point cloud. Finally, the return current cables, catenary cables, and contact cables in the OCS were accurately classified and extracted. To verify the accuracy of the method presented in this paper, we compared the experimental results of this article with the classification results of TerraSolid, and the classification results were evaluated in terms of four accuracy indicators. According to statistics, the average accuracy of using this method to extract two sets of OCS point clouds is 99.83% and 99.89%, respectively; the average precision is 100% and 99.97%, respectively; the average recall is 99.16% and 99.42%, respectively; and the average overall accuracy is 99.58% and 99.69% respectively, which is overall better than TerraSolid. The experimental results showed that this approach could accurately and quickly extract the complete OCS from the point cloud. It provides a new method for processing railway OCS point clouds and has high engineering application value in railway component detection.<\/jats:p>","DOI":"10.3390\/s21154961","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T22:37:14Z","timestamp":1626993434000},"page":"4961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Railway Overhead Contact System Point Cloud Classification"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4922-4174","authenticated-orcid":false,"given":"Xiao","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"National and Local Joint Engineering Laboratory of Safe Space Information Technology for High-Speed Railway Operation, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Road and Bridge Engineering, Sichuan Vocational and Technical College of Communications, Chengdu 611130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8756-2211","authenticated-orcid":false,"given":"Wei","family":"Xiang","sequence":"additional","affiliation":[{"name":"Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0809-7682","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"National and Local Joint Engineering Laboratory of Safe Space Information Technology for High-Speed Railway Operation, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40534-017-0148-4","article-title":"Advances of research on high-speed railway catenary","volume":"26","author":"Liu","year":"2018","journal-title":"J. Mod. Transport."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, C., Yang, B., Song, S., Peng, X., and Huang, R. (2018). Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10040613"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wu, G., Gao, G., Wei, W., and Yang, Z. (2019). Diagnosis and Detection of Service Performance of Pantograph and Catenary. The Electrical Contact of the Pantograph-Catenary System, Springer.","DOI":"10.1007\/978-981-13-6589-8"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, L., Xu, C., Lin, S., Li, S., and Tu, X. (2020). A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR. Sensors, 20.","DOI":"10.3390\/s20082224"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gao, S.B., Liu, Z.G., and Yu, L. (2017, January 12\u201314). Detection and monitoring system of the pantograph-catenary in high-speed railway (6C). Proceedings of the 7th International Conference on Power Electronics Systems and Applications\u2014Smart Mobility, Power Transfer & Security (PESA), Hong Kong, China.","DOI":"10.1109\/PESA.2017.8277746"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.neucom.2018.10.107","article-title":"Deep Learning-based Visual Ensemble Method for High-Speed Railway Catenary Clevis Fracture Detection","volume":"396","author":"Han","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lin, S., Xu, C., Chen, L., Li, S., and Tu, X. (2020). LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning. Sensors, 20.","DOI":"10.3390\/s20082212"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, Z. (2017). Detection and Estimation Research of High-speed Railway Catenary, Springer.","DOI":"10.1007\/978-981-10-2753-6"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"169","DOI":"10.2219\/rtriqr.41.169","article-title":"Overhead Contact Line Inspection System by Rail-and-Road Car","volume":"41","author":"Kusumi","year":"2000","journal-title":"RTRI"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, K., Siew, W.H., Stewart, R.W., and Wang, Y. (2008, January 18\u201320). Smart wireless railway monitoring system. Proceedings of the 4th IET International Conference on Railway Condition Monitoring, Derby, UK.","DOI":"10.1049\/ic:20080356"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, X., Qin, F., Xia, C., Bao, J., Huang, Y., and Zhang, X. (2019). An Innovative Detection Method of High-Speed Railway Track Slab Supporting Block Plane Based on Point Cloud Data from 3D Scanning Technology. Appl. Sci., 9.","DOI":"10.3390\/app9163345"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zou, R., Fan, X., Qian, C., Ye, W., Zhao, P., Tang, J., and Liu, H. (2019). An Efficient and Accurate Method for Different Configurations Railway Extraction Based on Mobile Laser Scanning. Remote Sens., 11.","DOI":"10.3390\/rs11242929"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Soil\u00e1n, M., S\u00e1nchez-Rodr\u00edguez, A., del R\u00edo-Barral, P., Perez-Collazo, C., Arias, P., and Riveiro, B. (2019). Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring. Infrastructures, 4.","DOI":"10.3390\/infrastructures4040058"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Rodr\u00edguez, A., Soil\u00e1n, M., Cabaleiro, M., and Arias, P. (2019). Automated Inspection of Railway Tunnels\u2019 Power Line Using LiDAR Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11212567"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tu, X., Xu, C., Liu, S., Lin, S., Chen, L., Xie, G., and Li, R. (2020). LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection. Sensors, 20.","DOI":"10.3390\/s20216387"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Azevedo, F., Dias, A., Almeida, J., Oliveira, A., Ferreira, A., Santos, T., Martins, A., and Silva, E. (2019). LiDAR-Based Real-Time Detection and Modeling of Power Lines for Unmanned Aerial Vehicles. Sensors, 19.","DOI":"10.3390\/s19081812"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chasco-Hern\u00e1ndez, D., Sanz-Delgado, J.A., Garc\u00eda-Morales, V., and \u00c1lvarez-Mozos, J. (2020). Automatic Detection of High-Voltage Power Lines in LiDAR Surveys Using Data Mining Techniques. Advances in Design Engineering, Proceedings of the XXIX International Congress INGEGRAF, Logro\u00f1o, Spain, 20\u201321 June 2019, Springer International Publishing.","DOI":"10.1007\/978-3-030-41200-5_62"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14916","DOI":"10.3390\/rs71114916","article-title":"Automated Recognition of Railroad Infrastructure in Rural Areas from LIDAR Data","volume":"7","author":"Arastounia","year":"2015","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, C., Yang, Z., Chen, Y., and Li, J. (2016, January 12\u201319). Automatic Railway Power Line Extraction Using Mobile Laser Scanning Data. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIII ISPRS Congress, Prague, Czech Republic.","DOI":"10.5194\/isprsarchives-XLI-B5-615-2016"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jun, J., Chen, L.H., Sohn, G., Luo, C., and Won, J. (2016). Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data. Remote Sens., 8.","DOI":"10.20944\/preprints201609.0088.v1"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Arastounia, M. (2017). An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds. Infrastructures, 2.","DOI":"10.3390\/infrastructures2020008"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ariyachandra, M., and Brilakis, I. (2020, January 27\u201328). Digital Twinning of Railway Overhead Line Equipment from Airborne LiDAR Data. Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC), Kitakyushu, Japan.","DOI":"10.22260\/ISARC2020\/0174"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guti\u00e9rrez-Fern\u00e1ndez, A., Fern\u00e1ndez-Llamas, C., Matell\u00e1n-Olivera, V., and Su\u00e1rez-Gonz\u00e1lez, A. (2020). Automatic Extraction of Power Cables Location in Railways Using Surface LiDAR Systems. Sensors, 20.","DOI":"10.3390\/s20216222"},{"key":"ref_24","unstructured":"Qi, C.R., Su, H., Mo, K.C., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_25","unstructured":"Qi, C.R., Su, H., and Guibas, L.J. (2017, January 4\u20139). PointNet++: Deep hierarchical feature learning on point sets in a metric space. Proceedings of the Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Poux, F., and Billen, R. (2019). Voxel-based 3D point cloud semantic segmentation: Unsupervised geometric and relationship featuring vs deep learning methods. ISPRS Int. J. Geo Inf., 8.","DOI":"10.3390\/ijgi8050213"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/TVCG.2018.2889944","article-title":"Semantic Labeling and Instance Segmentation of 3D Point Clouds using Patch Context Analysis and Multiscale Processing","volume":"26","author":"Hu","year":"2018","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4790","DOI":"10.1109\/TGRS.2016.2551546","article-title":"Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data","volume":"54","author":"Nurunnabi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.3390\/rs5041624","article-title":"Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation","volume":"5","author":"Aijazi","year":"2013","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.autcon.2014.12.015","article-title":"Segmentation of building point cloud models including detailed architectural\/structural features and MEP systems","volume":"51","author":"Dimitrov","year":"2015","journal-title":"Autom. Constr."},{"key":"ref_31","first-page":"88","article-title":"Octree-based region growing for point cloud segmentation. ISPRS J. Photogramm","volume":"104","author":"Vo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1236021","DOI":"10.1155\/2020\/1236021","article-title":"Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures","volume":"2020","author":"Tang","year":"2020","journal-title":"Adv. Civ. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.optlaseng.2019.06.011","article-title":"High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm","volume":"122","author":"Chen","year":"2019","journal-title":"Opt. Lasers Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s13218-010-0059-6","article-title":"Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments","volume":"24","author":"Rusu","year":"2010","journal-title":"KI K\u00fcnstliche Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liberti, L., and Lavor, C. (2017). Euclidean Distance Geometry, Springer.","DOI":"10.1007\/978-3-319-60792-4"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Demantk\u00e9, J., Mallet, C., David, N., and Vallet, B. (2011, January 29\u201331). Dimensionality based scale selection in 3d lidar point clouds. Proceedings of the ISPRS\u2014International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, Calgary, AB, Canada.","DOI":"10.5194\/isprsarchives-XXXVIII-5-W12-97-2011"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogr. Remote Sens."},{"key":"ref_38","first-page":"17","article-title":"A classification method for mobile laser scanning data based on object feature extraction","volume":"1","author":"Li","year":"2012","journal-title":"Remote Sens. Land Resour."},{"key":"ref_39","first-page":"126","article-title":"3D classification of power-line scene from airborne laser scanning data using random forests","volume":"38","author":"Kim","year":"2010","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_40","first-page":"21","article-title":"Power line classification from airborne LiDAR data via multi-scale neighborhood features","volume":"4","author":"Wang","year":"2019","journal-title":"Bull. Surv. Mapp."},{"key":"ref_41","unstructured":"Ester, M., Kriegel, H.P., and Xu, X.W. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the KDD\u201996: Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, USA. Available online: https:\/\/dblp.org\/rec\/conf\/kdd\/EsterKSX96."},{"key":"ref_42","first-page":"50","article-title":"Review and progress of DBSCAN research on spatial density clustering pattern mining method","volume":"43","author":"Fu","year":"2018","journal-title":"Sci. Surv. Mapp."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhu, L. (2019). Improvement of DBSCAN Algorithm Based on Adaptive Estimation of Eps Parameters and Its Application in Outlier Detection, Yunnan University.","DOI":"10.1145\/3302425.3302493"},{"key":"ref_44","first-page":"37","article-title":"Application of density clustering and pca point cloud processing in high-speed rail track detection","volume":"64","author":"Xiao","year":"2020","journal-title":"Railw. Stand. Des."},{"key":"ref_45","first-page":"2103","article-title":"3D point cloud segmentation, classification and recognition algorithm of railway scene","volume":"38","author":"Guo","year":"2017","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_46","unstructured":"Ba, J.J. (2019). Research on Anomaly Detection Method Based on Dbscan Algorithm, Civil Aviation University of China."},{"key":"ref_47","unstructured":"Goutte, C., and Gaussier, E. (2005, January 21\u201323). A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation. Proceedings of the 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/4961\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:32:54Z","timestamp":1760164374000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/4961"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,21]]},"references-count":47,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21154961"],"URL":"https:\/\/doi.org\/10.3390\/s21154961","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,21]]}}}