using thermal imagery.

Grant Number: DTRT12-G-UTC18
NURail Project ID: NURail2013-MTU-R06
One of the most important safety-related tasks in the rail industry is early detection of defective rolling
stock components. Railway wheels and wheel bearings are two components prone to damage due to
their interactions with brakes and railway track, which makes them a high priority when the rail
industry investigates improvements to current detection processes. One of the specific wheel defects is
a flat wheel, which is often caused by sliding during a heavy braking application. The main
contribution of this research work is development of a computer vision method for automatically
detecting the sliding wheels from images taken by wayside thermal cameras. As a byproduct, the
process will also include a method for detecting hot bearings from the same images. We trained our
algorithm with a set of simulated data and tested it on several thermal images collected in in North
American revenue service by the Union Pacific Railroad (UPRR).