Error correction method based on projection transformation for rail wear measurement using line-structured light

The maintenance of railway rails relies heavily on accurate profiling and wear assessment. In this research, a rail profile detection system using line-structured light machine vision technology is developed. Traditional image processing algorithms for rail profile measurement involve Zhang’s camera calibration, radial distortion correction, Gaussian filtering, and the iterative closest point (ICP) algorithm for point cloud registration. Building upon these conventional algorithms, an integrated error correction framework comprising projective transformation and system offset compensation is proposed. We introduce a method to dynamically determine the direction vector of the rail alignment and the angle between the laser plane and the rail cross-section for the projective transformation. Compared to the same hardware system without error correction, this method improves measurement accuracy from 0.0686 to ±0.015mm at the lateral wear measurement points and from 0.0678 to ±0.020mm at the vertical wear measurement points in profile detection.