Abstract
Ground penetrating radar (GPR) has been used for several years as a non-contact and non-destructive measurement method for rail track analysis with the aim of recording the condition of ballast and substructures. As the recorded data sets typically cover a distance of many kilometers, the evaluation of these data involves considerable effort and costs. For this reason, there is an increasing need for automated support in the evaluation of GPR measurement data. This paper presents an image segmentation pipeline based on 2D Gabor filter texture analysis, which can assist users in GPR data-based track condition assessment. Gabor filtering is used to transform a radargram image (or B-scan) into a high-dimensional, multi-resolution representation. Principal component analysis (PCA) is then applied to reduce the data content to three characteristic dimensions (namely amplitude, frequency, and local scattering) to finally obtain a segmented radargram image representing different classes of relevant image structures. From these results, quantitative measures can be derived that allow experts an improved condition assessment of the rail track.
Highlights
Data acquisition by means of ground penetrating radar (GPR) in railway track maintenance is an established, non-destructive measurement method for monitoring ballast conditions and other parameters [1,2,3,4]
With many radargram image structures becomes possible in a level of detail that far exceeds the years of experience in the interpretation of such measurement data, experts from this purely visual capabilities of have a human observer, e.g., for determining the exact The course of of company accompanied the development of the presented method
The presented form of automatic texture segmentation based on 2D Gabor filtering contributes to reducing the complexity of interpreting and quantifying radargrams in rail track condition assessment
Summary
Data acquisition by means of ground penetrating radar (GPR) in railway track maintenance is an established, non-destructive measurement method for monitoring ballast conditions and other parameters [1,2,3,4]. Several kilometers), and Different the y-axis shows depth These methods are based on time times). With many radargram image structures becomes possible in a level of detail that far exceeds the years of experience in the interpretation of such measurement data, experts from this purely visual capabilities of have a human observer, e.g., for determining the exact The course of of company accompanied the development of the presented method. A summary andofoutlook are given in of a human observer, e.g., forFinally, determining the exact course ground structure boundaries
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