Big Data Framework for Rail Inspection

Abstract

It is necessary that periodical inspection and maintenance of railway transportation systems, which is becoming more and more common day by day, is required. A typical rail line can range from a few kilometers to thousands of kilometers. It is not possible to control this length of railway line with human labor. For this purpose, rail inspection is done automatically by machine vision today. The input data of machine vision systems consists of data from high-resolution cameras and other sensors. These data are evaluated by machine learning methods and the diagnosis result is produced. However, the data rate and the amount of data that occur in both long-distance and long-time repetitive ray inspection applications are huge. Proper handling, storage and analysis of this data requires a Big Data-based approach. In this study, an approach is proposed for the evaluation of large data obtained from vision-based diagnostic systems and the extraction of useful information in tracked systems. The proposed approach has been verified using simulation and experimental data and the effectiveness of the approach, utility, usability, and other visual-based diagnostic approaches to be developed in directed systems have been demonstrated.