Computer Science · Computer Vision and Pattern Recognition
Researchers introduced a 13,314-image dataset for testing AI systems that view urban intersections from above. The results show that detection can run on edge devices such as the Jetson Orin Nano, although pedestrians remain harder to detect than vehicles and performance changes with scene conditions.
Researchers developed an AI system designed to detect small objects in complex aerial images from drones. On three image datasets, it improved detection measures over the comparison system while maintaining real-time processing at 68.7 frames per second.
A new open-source pipeline uses an existing AI system to segment far more of very large Earth-observation images without training data or manual threshold tuning. Across seven scenes, it increased coverage to 91–98% and joined objects split across image tiles.
A lightweight AI system detected marine organisms in degraded underwater images with lower computing demands than YOLOv8m. Tests on a dataset and an embedded computer suggest it could support faster, more energy-efficient monitoring on autonomous underwater vehicles.
