Deep learning on rail profiles matching. (arXiv:2205.08687v2 [cs.CV] UPDATED)

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 …

arxiv cv deep learning learning profiles rail