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A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR.

Lipei Chen¹, Cheng Xu¹, Shuai Lin¹

¹College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.

Sensors (Basel, Switzerland)

|April 25, 2020

Summary

This summary is machine-generated.

A new deep learning method accurately identifies overhead contact system (OCS) components from 2D LiDAR point clouds. This automated approach enhances railway safety inspections by enabling precise semantic segmentation of critical infrastructure.

Keywords:

LiDAR MLS OCS deep learning point cloud railway

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Area of Science:

Railway Engineering
Computer Vision
Artificial Intelligence

Background:

Overhead contact systems (OCS) are vital for train power supply, requiring regular inspections to ensure safety.
Analyzing 2D LiDAR point clouds is a key method for OCS inspection, but recognizing components is challenging due to system complexity.
Current methods struggle with the accurate semantic segmentation of diverse OCS components from point cloud data.

Purpose of the Study:

To develop an effective deep learning-based method for semantic segmentation of OCS components from mobile 2D LiDAR point clouds.
To address the challenges posed by the complex composition of OCS in accurate component recognition.
To enable both online and batch processing for flexible OCS data analysis.

Main Methods:

A novel deep learning approach for semantic segmentation of 2D LiDAR point clouds.
An iterative point partitioning algorithm for enhanced local feature extraction.
A Spatial Fusion Network module designed for multi-scale local feature learning.
Scan line by scan line processing for classifying points into object categories.

Main Results:

The proposed method achieved high accuracy in recognizing sixteen categories of OCS components.
Mean Intersection-over-Unions (mIoUs) reached 96.12% for online data processing.
Mean Intersection-over-Unions (mIoUs) reached 97.17% for batch data processing.

Conclusions:

The deep learning method effectively performs semantic segmentation of OCS components from 2D LiDAR data.
The approach demonstrates significant potential for improving the efficiency and accuracy of railway infrastructure inspections.
The developed technique offers a robust solution for recognizing multiple OCS components in complex railway environments.