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Sensors 2017, 17(8), 1791; doi:10.3390/s17081791

A 3D Laser Profiling System for Rail Surface Defect Detection

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
3
School of Computer Science, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 4 July 2017 / Revised: 31 July 2017 / Accepted: 2 August 2017 / Published: 4 August 2017
(This article belongs to the Section Remote Sensors)
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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. View Full-Text
Keywords: rail surface defect; defect detection; iterative closest point; laser imaging rail surface defect; defect detection; iterative closest point; laser imaging
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Xiong, Z.; Li, Q.; Mao, Q.; Zou, Q. A 3D Laser Profiling System for Rail Surface Defect Detection. Sensors 2017, 17, 1791.

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