  Matching the rail cross-section profiles measured on site with the designed
profile is a must to evaluate the wear of the rail, which is very important for
track maintenance and rail safety. So far, the measured rail profiles to be
matched usually have four features, that is, large amount of data, diverse
section shapes, hardware made errors, and human experience needs to be
introduced to solve the complex situation on site during matching process.
However, traditional matching methods based on feature points or feature lines
could no longer meet the requirements. To this end, we first establish the rail
profiles matching dataset composed of 46386 pairs of professional manual
matched data, then propose a general high-precision method for rail profiles
matching using pre-trained convolutional neural network (CNN). This new method
based on deep learning is promising to be the dominant approach for this issue.
Source code is at
https://github.com/Kunqi1994/Deep-learning-on-rail-profile-matching.
