Matlab Implementation of Machine Vision Algorithm on Ballast Degradation Evaluation
This work addresses the need for efficient maintenance in the U.S. railway system by offering a potential automated inspection method, though it appears incremental as it adapts existing computer vision techniques to a specific domain.
The paper tackles the labor-intensive problem of evaluating railway ballast degradation by developing a machine vision algorithm in Matlab that analyzes ballast images to produce an index correlating with the traditional Fouling Index, aiming to replace time-consuming mechanical methods.
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.