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For rail wear measurement, the process of profile registration is critical. Nowadays, the widely used method is the rail waist double circle segment method (DCS). However, this method may be invalid in actual applications because of the influence of the following two factors: one is the outliers mixed into profiles and the other is the profile diversity. The former induces the misalignment between the measured profile and the standard one, and the latter may destroy the integrity of rail waist, which makes that the measured profile cannot be matched with the standard one through DCS. To solve the two problems, a hybrid profile registration method is proposed in this paper. First, by locating new matching primitives to realize coarse registration, we check the profile integrity fast. Then, for complete profile, we rematch its original measured profile with the standard one finely based on DCS to detect and remove the outliers correctly. The efficiency and superiority of the proposed method were verified by numerous experiments. The results show that the average score of F1-Measure for outlier detection reaches 0.95, which outperforms some classical models obviously. Meanwhile, the result of the profile integrity check is also basically coincided with the real face of test zones. Furthermore, the system can run at a speed of 21.95 km/h under our experimental setup, which is far higher than that of the rail maintenance train (up to 5 km/h). [ABSTRACT FROM AUTHOR]

Dynamically measuring rail profile using the structured-light vision suffers from random vibrations on the line laser projector, which would cause distorted rail profiles. This paper presents a simple and effective distortion rectifying method to address this issue. The distorted rail profile is rectified by easily projecting it onto an auxiliary plane which is parallel to the cross section of rail. In order to establish the auxiliary plane, three profiles formed by radiating multiline structured light on rail are used to fit the rail longitudinal axis. More importantly, only one of the light planes is required to be calibrated beforehand. The others are calibrated online with the proposed self-calibration method, which is based on collinearity and parallelity constraints on the scene points of different rail profiles and requires only one image of the scene. Apart from evaluating the implementation with comprehensive experiments, we compare our method against other published works. The results exhibit its effectiveness and superiority in terms of the dynamic measurement of the rail profile. [ABSTRACT FROM AUTHOR]

Rail section profile accurate measurement techniques are critical to guarantee the transport security and reduce the maintenance cost. However, the profile may make an affine transformation under vehicle vibration. It is a challenge to identify and calibrate the affine distorted profiles in the 2-D laser displacement system (LDS). By profound observation and discussion on triple primitives including rail jaw, railhead inner line, and rail waist curve, we found the difference between normal profiles and distorted ones. To measure the difference quantitatively, we proposed a novel curve similarity measurement method, i.e., point set mapping (PSM), in this paper. By constructing PSM from the standard profile to the measured one on the triple matching primitives, the fast recognition and accurate calibration of rail profile under affine distortion were implemented. The accuracy and efficiency of the system were verified by experiments and comparisons with related methods. The results show that the PSM is faster and more robust than the universal Hausdorff distance on the similarity measure, and the performance of the modified particle swarm optimization is superior for profile distortion calibration. Furthermore, the LDS can run at a speed of 12.86 km/h under our experimental setup, which is far higher than that of the rail maintenance train (up to 5 km/h). [ABSTRACT FROM PUBLISHER]