Advancements in Railroad Track Inspection Using Machine-Vision Technology

Railroad engineering practices and Federal Railroad Administration (FRA) regulations require track to be inspected for physical defects at specified intervals, which may be as often as twice per week. Currently, most of these inspections are manual and are conducted visually by railroad track inspectors. Inspections include detecting defects relating to the ties, fasteners, rail, special trackwork and ballast section. Enhancements to the current manual inspection process are possible using advanced technologies such as machine vision, which consists of recording digital images of track elements of interest and analyzing them using custom algorithms to identify defects or their symptoms. Based on analysis of FRA accident data, discussion with railroad track engineering experts and consultation with Association of American Railroads (AAR) researchers, this project focuses on using machine vision to detect irregularities and defects in cut spikes, rail anchors, turnout components and the crib ballast. Because inspection data will be stored digitally, comparative and trend analyses of track component condition are possible through data mining and Information Technology (IT) procedures. These capabilities will facilitate longer-term predictive assessment of the health o