LiDAR Point Cloud Recognition and Visualization with Deep Learning for Overhead Contact Inspection.

Xiaohan Tu,Siping Liu,Renfa Li,Guoqi Xie,Lipei Chen,Shuai Lin,Cheng Xu

doi:10.3390/s20216387

Abstract

As overhead contact (OC) is an essential part of power supply systems in high-speed railways, it is necessary to regularly inspect and repair abnormal OC components. Relative to manual inspection, applying LiDAR (light detection and ranging) to OC inspection can improve efficiency, accuracy, and safety, but it faces challenges to efficiently and effectively segment LiDAR point cloud data and identify catenary components. Recent deep learning-based recognition methods are rarely employed to recognize OC components, because they have high computational complexity, while their accuracy needs to be improved. To track these problems, we first propose a lightweight model, RobotNet, with depthwise and pointwise convolutions and an attention module to recognize the point cloud. Second, we optimize RobotNet to accelerate its recognition speed on embedded devices using an existing compilation tool. Third, we design software to facilitate the visualization of point cloud data. Our software can not only display a large amount of point cloud data, but also visualize the details of OC components. Extensive experiments demonstrate that RobotNet recognizes OC components more accurately and efficiently than others. The inference speed of the optimized RobotNet increases by an order of magnitude. RobotNet has lower computational complexity than other studies. The visualization results also show that our recognition method is effective.

Highlights

As an important part of overhead catenary (OC) systems in high-speed railways, the catenary assumes the task of transmitting the electric energy from traction substations to trains
We summarize the contribution of this paper as follows: (1) We propose a lightweight model, RobotNet, which can be deployed on robots
We propose the attention module inspired by Woo et al [31], improving accuracy in image detection and classification tasks

Summary

Introduction

As one of the indispensable modes of transportation, high-speed railways have great significance to social and economic development. The scale of high-speed railways is expanding, and it is critical to ensure the safe and reliable operation of high-speed railways. As an important part of overhead catenary (OC) systems in high-speed railways, the catenary assumes the task of transmitting the electric energy from traction substations to trains. The OC system includes the contact wire, dropper, steady arm, registration arm, catenary wire, pole, cantilever, and insulator. The status of the catenary equipment is closely related to the normal operation of trains. The faults and geometric parameters of the above components directly affect the safety of the train

Methods

Results

Conclusion