  America has a massive railway system. As of 2006, U.S. freight railroads have
140,490 route- miles of standard gauge, but maintaining such a huge system and
eliminating any dangers, like reduced track stability and poor drainage, caused
by railway ballast degradation require huge amount of labor. The traditional
way to quantify the degradation of ballast is to use an index called Fouling
Index (FI) through ballast sampling and sieve analysis. However, determining
the FI values in lab is very time-consuming and laborious, but with the help of
recent development in the field of computer vision, a novel method for a
potential machine-vison based ballast inspection system can be employed that
can hopefully replace the traditional mechanical method. The new machine-vision
approach analyses the images of the in-service ballasts, and then utilizes
image segmentation algorithm to get ballast segments. By comparing the segment
results and their corresponding FI values, this novel method produces a
machine-vision-based index that has the best-fit relation with FI. The
implementation details of how this algorithm works are discussed in this
report.
