1 citations · 2 across the 8 of their papers we have counts for
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Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles
Luca de Martino, Federico Aromolo, Federico Nesti +1
Real-time anomaly segmentation is essential for the safety of autonomous systems. Although recent approaches offer high accuracy, their computational cost limits their deployment o…
Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments
Enrico Francesco Giannico, Federico Nesti, Gianluca D'Amico +5
Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and flexible system that can bot…
OSDaR-AR: Enhancing Railway Perception Datasets via Multi-modal Augmented Reality
Federico Nesti, Gianluca D'Amico, Mauro Marinoni +1
Although deep learning has significantly advanced the perception capabilities of intelligent transportation systems, railway applications continue to suffer from a scarcity of high…
Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox
Xavier Diaz, Gianluca D'Amico, Raul Dominguez-Sanchez +3
In recent years, interest in automatic train operations has significantly increased. To enable advanced functionalities, robust vision-based algorithms are essential for perceiving…