A 3D Laser Profiling System for Rail Surface Defect Detection.

Zhimin Xiong,Qingzhou Mao,Qin Zou,Qingquan Li

doi:10.3390/s17081791

Abstract

Rail surface defects such as the abrasion, scratch and peeling often cause damages to the train wheels and rail bearings. An efficient and accurate detection of rail defects is of vital importance for the safety of railway transportation. In the past few decades, automatic rail defect detection has been studied; however, most developed methods use optic-imaging techniques to collect the rail surface data and are still suffering from a high false recognition rate. In this paper, a novel 3D laser profiling system (3D-LPS) is proposed, which integrates a laser scanner, odometer, inertial measurement unit (IMU) and global position system (GPS) to capture the rail surface profile data. For automatic defect detection, first, the deviation between the measured profile and a standard rail model profile is computed for each laser-imaging profile, and the points with large deviations are marked as candidate defect points. Specifically, an adaptive iterative closest point (AICP) algorithm is proposed to register the point sets of the measured profile with the standard rail model profile, and the registration precision is improved to the sub-millimeter level. Second, all of the measured profiles are combined together to form the rail surface through a high-precision positioning process with the IMU, odometer and GPS data. Third, the candidate defect points are merged into candidate defect regions using the K-means clustering. At last, the candidate defect regions are classified by a decision tree classifier. Experimental results demonstrate the effectiveness of the proposed laser-profiling system in rail surface defect detection and classification.

Highlights

The rail surface defect is an important factor that affects the operation safety of rails
The research and development of automatic methods for rail surface defect detection is very important, which on the one hand achieves an effective maintenance of the train-operation safety by timely and accurate defect location and recognition and, on the other hand, improves the passenger comfort and environmental protection, reduces noise and the energy loss caused by rail defects
This paper proposes an adaptive iterative closest point (AICP) algorithm, using the Kalman filter model to predict the transformation of the current profile through the recursion of the adjacent continuous profiles and calculating the average distance between each part of point cloud and the standard point cloud under this transformation parameter and, at last, choosing the corresponding optimal transformation parameter by evaluating these average distances

Summary

Introduction

The rail surface defect is an important factor that affects the operation safety of rails. Common rail surface defects such as abrasion, corrugation, scratch, corrosion and peeling would cause damage to the train wheels and the rail bearings, which will shorten the service life of train parts, and bring a potential critical safety crisis to the trains. The research and development of automatic methods for rail surface defect detection is very important, which on the one hand achieves an effective maintenance of the train-operation safety by timely and accurate defect location and recognition and, on the other hand, improves the passenger comfort and environmental protection, reduces noise and the energy loss caused by rail defects. Automatic detection of defects from rail surface images is a very challenging problem. From the perspective of texture analysis, filtering and wavelet transform-based approaches are widely used for defects detection by analyzing the local spatial patterns, e.g., the Gabor filter, curvelet transform

Methods

Results

Conclusion