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cs.CV2026
AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving
Fabrizio Genilotti, Arianna Stropeni, Gionata Grotto +4
The reliability of a machine vision system for autonomous driving depends heavily on its training data distribution. When a vehicle encounters significantly different conditions, s…
cs.CV2026
Efficient Visual Anomaly Detection at the Edge: Enabling Real-Time Industrial Inspection on Resource-Constrained Devices
Arianna Stropeni, Fabrizio Genilotti, Francesco Borsatti +3
Visual Anomaly Detection (VAD) is essential for industrial quality control, enabling automatic defect detection in manufacturing. In real production lines, VAD systems must satisfy…
cs.CV2026
VAD4Space: Visual Anomaly Detection for Planetary Surface Imagery
Fabrizio Genilotti, Arianna Stropeni, Francesco Borsatti +3
Space missions generate massive volumes of high-resolution orbital and surface imagery that far exceed the capacity for manual inspection. Detecting rare phenomena is scientificall…