Use of a 3D model to improve the performance of laser-based railway track inspection

Abstract

In recent decades, 3D reconstruction techniques have been applied in an increasing number of areas such as virtual reality, robot navigation, medical imaging and architectural restoration of cultural relics. Most of the inspection techniques used in railway systems are, however, still implemented on a 2D basis. This is particularly true of track inspection due to its linear nature. Benefiting from the development of sensor technology and constantly improving processors, higher quality 3D model reconstructions are becoming possible which push the technology into more challenging areas. One such advancement is the use of 3D perceptual techniques in railway systems. This paper presents a novel 3D perceptual system, based on a low-cost 2D laser sensor, which has been developed for the detection and characterisation of physical surface defects in railway tracks. An innovative prototype system has been developed to capture and correlate the laser scan data; dedicated 3D data processing procedures have then been developed in the form of three specific defect-detection algorithms (depth gradient, face normal and face-normal gradient) which are applied to the 3D model. The system has been tested with rail samples in the laboratory and at the Long Marston Railway Test Track. The 3D models developed represent the external surface of the samples both laterally (2D slices) and longitudinally (3D model), and common surface defects can be detected and represented in 3D. The results demonstrate the feasibility of applying 3D reconstruction-based inspection techniques to railway systems.

Method for automatic railway track surface defect classification and evaluation using a laser‐based 3D model

“…1 b ). Other specifications have been introduced in previous explorative research [14], so it is unnecessary to go into details here.…”

“…The 2D profiles‐based point cloud data is reconstructed to a 3D model, on which multiple geometrical features of the rail surface [depth gradient (DGD), face normal (FND), and face normal gradient (FNGD)] are generated. Each geometrical feature has been shown to align with the detection of a specific type of RCF defect (cracks, squats, or shelling) [14]. Compared to the defect detection methods mentioned above, 3D model‐based defect detection neither relies on comparisons with standard models nor uses thresholds, but analyses the geometrical features of the defects in 3D, and explores their different characteristics such as the depth gradient of the nearest points and the face normal vector of each mesh element in different categories of defect, which are not accessible from 2D data sets.…”

Perspectives for the development of high-speed transport

“…Технология лазерных 3D сканеров позволяет собирать миллионы измеряемых точек данных, от измерений до пространственных связей объектов, в течение нескольких секунд. Это значительно сокращает время, которое было бы потрачено в противном случае, исключает вероятность сбора неточных данных и, в частности, помогает в сложных проектах [10].…”

Application of laser scanning technology for structure gauge measurement

“…Data obtained with various measuring sensors such as vision systems based on video cameras and thermal imaging cameras constitute a valuable complement to point clouds originating from laser scanning. Data from these systems can be employed, among others, for the analysis of structure gauge [11], the location of track faults [12,13], the wear analysis of the overhead power traction cables [14,15] and the location of short circuits [16].…”