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Accurate railway obstacle detection is now possible thanks to a modular framework that fuses LiDAR and monocular depth estimation, achieving sub-meter distance accuracy.
Bridging the sim-to-real gap in railway perception, a new augmented reality framework generates realistic training data by seamlessly integrating virtual objects into real-world railway scenes, outperforming traditional methods.