Abstract
We present an approach to high-resolution rail surface analysis combining 2D image texture classification and 21/2D analysis of surface disruptions. Detailed analysis of images of rail surfaces is used to observe the condition of rails and, as a precaution, to avoid rail breaks and further damage. Single rails are observed by a color line scan camera at high resolution of approximately 0.2 millimeters and under special illumination in order to enable 21/2D image analysis. Gabor filter banks are used for 2D texture description and classes are modeled by Gaussian mixtures. A Bayesian classifier, which also incorporates background knowledge, is used to differentiate between surface texture classes. Classes which can be related to surface disruptions are derived from the analysis of the anti-correlation properties between two color channels. Images are illuminated by two light sources mounted at different position and operating at different wavelengths. Results for data gathered in the Vienna metro system are presented.
This work is supported by the Austrian Federal Ministry for Transport, Innovation and Technology BMVIT, program line I2V ”Intermodalität und Interoperabilität von Verkehrssystemen”, project fractINSPECT.
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Huber-Mörk, R., Nölle, M., Oberhauser, A., Fischmeister, E. (2010). Statistical Rail Surface Classification Based on 2D and 21/2D Image Analysis. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_6
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