Summary: Monitoring the structural health of heavy-haul rolling stock is critical to ensuring safe and efficient railroad operation. As a result, periodic manual inspections are required to detect structural damage and defects. These inspections rely heavily on the acuity, knowledge and endurance of qualified inspection personnel. There is the potential to enhance inspection effectiveness and efficiency through machine vision technology, which uses computer algorithms to convert digital image data into useful information. This paper describes research and development of an automated machine vision inspection system capable of detecting structural defects in freight car underframes
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