Predicting Ballast Fouling Conditions Through RGB-Based Statistical Quantity Analysis

Abstract

Evaluating railway ballast fouling is critical to assessing track conditions and planning proper ballast maintenance. Varying material properties associated with different ballast fouling conditions can be used to evaluate the severity of fouling. Although approaches using ground penetration radar, impulse response, surface wave, and SmartRock have been developed to estimate the fouling conditions, these methods require special sensors and equipment, or well-trained technicians. Recently, convolutional neural network (CNN) based computer vision approaches have become popular in performing particle segmentation to obtain the ballast grain size distribution. The coarse aggregate fraction can be evaluated by the CNN method, but it is not easy to segment fine particles accurately with these approaches. This study proposes a novel image analysis approach to directly estimate the ballast fouling conditions. First, fouled ballast images with different fouling conditions are taken as references. The RGB (red, green, blue) color distributions of the fouled ballast images are then processed through statistical analysis. A strong linear correlation is found between Fouling Index (FI) and variance and this is used to establish the FI prediction model. The established FI prediction model is tested and validated by additional fouled ballast samples. The FI prediction model proposed in this study can be used to quantify ballast fouling conditions with promising performance.