Modern demands for railway track measurements require high accuracy (about 2-5 mm) of rails placement along the track to ensure smooth, safe and fast transportation. As a mean for railways geometry measurements we suggest a stereoscopic system which measures 3D position of fiducial marks arranged along the track by image processing algorithms. The system accuracy was verified during laboratory tests by comparison with precise laser tracker indications. The accuracy of ±1.5 mm within a measurement volume 150×400×5000 mm was achieved during the tests. This confirmed that the stereoscopic system demonstrates good measurement accuracy and can be potentially used as fully automated mean for railway track inspection.

In this work we present a pattern recognition method based on geometry analysis of a flat pattern. The method provides reliable detection of the pattern in the case when significant perspective deformation is present in the image. The method is based on the fact that collinearity of the lines remains unchanged under perspective transformation. So the recognition feature is the presence of two lines, containing four points each. Eight points form two squares for convenience of applying corner detection algorithms. The method is suitable for automatic pattern detection in a dense environment of false objects. In this work we test the proposed method for statistics of detection and algorithm's performance. For estimation of pattern detection quality we performed image simulation process with random size and spatial frequency of background clutter while both translational (range varied from 200 mm to 1500 mm) and rotational (up to 60 • ) deformations in given pattern position were added. Simulated measuring system included a camera (4000x4000 sensor with 25 mm lens) and a flat pattern. Tests showed that the proposed method demonstrates no more than 1 % recognition error when number of false targets is up to 40.

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