Automated Survey of Railway Conditions: A Preliminary Investigation

Abstract

Railway condition survey is limited by available time, particularly for high-speed railway due to the need to use its full capacity every day. Therefore, railway condition survey at high-speed at high-automation level becomes necessary. In addition, different from highway pavement condition assessment, safety is the primary factor for railway condition survey. The railway survey frequency is usually daily or weekly, while highway or runway pavement surveys are commonly conducted on annual basis. Rail profile measurement in the transverse direction, missing or broken fastener detection and slab surface crack identification are three primary railway survey items. Currently, manual or semi-automated methods are becoming feasible for railway condition surveys, but most of the applications are based on low resolution of data and have poor precision and accuracy. The repeatability and reliability of their analysis results cannot be assured. Based on the successful applications of three-dimensional (3D) laser imaging for highways and runways, the team is developing a 3D digital railway surface evaluation system. This system consists of high-performance 3D laser profilers to acquire surface details in 3D of rail tracks and supporting elements and structures at resolutions finer than 1mm. A computer vision framework is being developed to acquire and process various positioning and visual data streams in a synchronous approach. This paper discusses the status of current railway survey technologies and limitations, presents the planned implementation of new 3D laser imaging technology for high-speed and complete coverage, and possible solutions to detecting rail surface defects. Especially, fastener inspection algorithm is being developed and tested. Preliminary results reveal that the proposed 3D system under development and the fastener recognition algorithms are substantially more capable in terms of high data quality and automation level for data analysis. The objective is to initially supplement and ultimately replace traditional methods of rail safety and condition evaluation.