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In this paper, we present a real-time automatic vision-based rail inspection system, which performs inspections at 16 km/h with a frame rate of 20 fps. The system robustly detects important rail components such as ties, tie plates, and anchors, with high accuracy and efficiency. To achieve this goal, we first develop a set of image and video analytics and then propose a novel global optimization framework...
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