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Article

Railway Overhead Contact System Point Cloud Classification

1
Faculty of Geoscience and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
National and Local Joint Engineering Laboratory of Safe Space Information Technology for High-Speed Railway Operation, Southwest Jiaotong University, Chengdu 611756, China
3
Department of Road and Bridge Engineering, Sichuan Vocational and Technical College of Communications, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of Xiao Chen, Guoxiang Liu, Zhen Chen, Zhuang Chen, Rui Zhang, Zhanguang Wu, Xianzhang Zhou, Railway contact point cloud classification extraction. In Proceedings of the Sixth National LiDAR Conference, International Conference Center of China University of Geosciences, Beijing, China, 20–22 November 2020.
Academic Editors: Ruofei Zhong, Zhizhong Kang, Xiaohuan Xi, Haili Sun and Jinhu Wang
Sensors 2021, 21(15), 4961; https://doi.org/10.3390/s21154961
Received: 15 June 2021 / Revised: 12 July 2021 / Accepted: 16 July 2021 / Published: 21 July 2021
(This article belongs to the Special Issue Selected Papers from The Sixth National LiDAR Conference)
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. View Full-Text
Keywords: railway OCS; point cloud; scale adaptive feature algorithm; DBSCAN algorithm; classification railway OCS; point cloud; scale adaptive feature algorithm; DBSCAN algorithm; classification
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MDPI and ACS Style

Chen, X.; Chen, Z.; Liu, G.; Chen, K.; Wang, L.; Xiang, W.; Zhang, R. Railway Overhead Contact System Point Cloud Classification. Sensors 2021, 21, 4961. https://doi.org/10.3390/s21154961

AMA Style

Chen X, Chen Z, Liu G, Chen K, Wang L, Xiang W, Zhang R. Railway Overhead Contact System Point Cloud Classification. Sensors. 2021; 21(15):4961. https://doi.org/10.3390/s21154961

Chicago/Turabian Style

Chen, Xiao, Zhuang Chen, Guoxiang Liu, Kun Chen, Lu Wang, Wei Xiang, and Rui Zhang. 2021. "Railway Overhead Contact System Point Cloud Classification" Sensors 21, no. 15: 4961. https://doi.org/10.3390/s21154961

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