Railway Overhead Contact System Point Cloud Classification

Abstract

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.

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

“…The main geometric features include linearity ( ), planarity ( ), anisotropy ( ), spherical dispersion ( ) and the normal vector ( ), volume density ( ), verticality ( ), roughness ( ), and so on. For a point cloud in a certain neighborhood, its covariance matrix and eigenvalues can be calculated from the coordinates of the point cloud [ 41 , 42 , 43 , 44 ], then the geometric features are calculated with the covariance matrix and eigenvalues. Volume density ( ) represents the sparseness of points in the neighborhood of the point cloud.…”

Enhancing Railway Detection by Priming Neural Networks with Project Exaptations

“…Similar ideas have also been applied to road infrastructure [19]. Chen et al [20] achieved remarkable results analysing the overhead catenary system. Their methodology is based around density-based spatial clustering.…”

Point Cloud Analysis of Railway Infrastructure: A Systematic Literature Review

“…Similarly Chen et al also use fixed thresholds to remove distant points with no information [34]. A more advanced method of detecting ground points is proposed by Chen et al which use a Euclidean distance clustering segmentation algorithm [35]. When point clouds are collected using a mobile scanner mounted on a train, the trajectory log can play an important role in the culling of points.…”

“…It is important to acknowledge that certain aspects of the railway infrastructure, such as foreign objects, bridges, and tunnel deformation, have not been included in this paper due to the set exclusion criteria. Nevertheless, these areas have been gaining interest, particularly in the context of predictive maintenance and the expansion of high-speed rail networks in China (e.g., [35]). As the railway industry continues to evolve, exploring these aspects becomes increasingly crucial for comprehensive railway infrastructure modelling and analysis.…”