Abstract

Overhead catenary system (OCS) automatic detection is of important significance for the safe operation and maintenance of electrified railways. The vehicle-borne mobile mapping system (VMMS) may significantly improve the data acquisition. This paper proposes a VMMS-based framework to realize the automatic detection and modelling of OCS. The proposed framework performed semantic segmentation, model reconstruction and geometric parameters detection based on LiDAR point cloud using VMMS. Firstly, an enhanced VMMS is designed for accurate data generation. Secondly, an automatic searching method based on a two-level stereo frame is designed to filter the irrelevant non-OCS point cloud. Then, a deep learning network based on multi-scale feature fusion and an attention mechanism (MFF_A) is trained for semantic segmentation on a catenary facility. Finally, the 3D modelling is performed based on the OCS segmentation result, and geometric parameters are then extracted. The experimental case study was conducted on a 100 km high-speed railway in Guangxi, China. The experimental results show that the proposed framework has a better accuracy of 96.37%, outperforming other state-of-art methods for segmentation. Compared with traditional manual laser measurement, the proposed framework can achieve a trustable accuracy within 10 mm for OCS geometric parameter detection.

Highlights

  • The fine stereo frame is cut along the track direction, and the catenary facility point cloud is extracted by clipping box CBox from the track data with the frame along the track, which satisfies the following equation:

  • The proposed deep learning network model is verified to realize the semantic segmentation of catenary facilities

  • The method achieves an automatic search and extraction of catenary facility point cloud information from the original 3D point cloud scene through the steps of positioning the dual selection stereo frame, a determination of the offset vector of the dual selection stereo frame and the automatic attitude adjustment of the selected stereo frame assisted by position and orientation system (POS) data along the rail

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Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The overhead catenary system (OCS) is an electromechanical system in the railway that provides electrical energy to an electric traction unit through a contact wire. The rapid growth of the railway construction brings tremendous challenges for this task Catenary geometric parameters, such as the height and stagger of the contact wire, are important data for evaluating the catenary status [4].

Related Works
Statistics-Based Method
Deep-Learning-Based Method
Material and Methodology
Study Area and VMMS Data Generation
Double Selection Stereo Frame of OCS
Deep Learning Based Semantic Segmentation
Refine Structure
Channel Feature Enhancement
Search Result
Segmentation Results
Quantitative Evaluation of the Segmentation Results
Parameter Complexity
Geometric Evaluation of Reconstruction Results
Conclusions and Future Works

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