Individual railroad track maintenance standards and the Federal Railroad Administration (FRA) Track Safety Standards require periodic inspection of railway infrastructure to ensure safe and efficient operation. This inspection is a critical, but labor-intensive task that results in large annual operating expenditures and has limitations in speed, quality, objectivity, and scope. To improve the cost-effectiveness of the current inspection process, machine vision technology can be developed and used as a robust supplement to manual inspections. One of the objectives of the research underway at the University of Illinois at Urbana-Champaign (UIUC) is to investigate the feasibility of using machine-vision technology to recognize turnout components, as well as the performance of algorithms designed to recognize and detect defects in other track components. In addition, to prioritize which components are the most critical for the safe operation of trains, a risk-based analysis of the FRA Accident Database was performed.
Machine Vision Inspection of Railroad Track. USDOT Region V Regional University Transportation Center Final Report
2011
46 pages
Report
Keine Angabe
Englisch
Railroad Transportation , Transportation Safety , Pattern Recognition & Image Processing , Railroad tracks , Inspection , Computer vision , Maintenance management , Image processing , Automation , Detection , Defects , Algorithms , Derailment , Safety , Standards , Accident prevention , Regulations , Implementation , Machine vision
Automatic Railroad Track Inspection
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