  This paper describes how advanced deep learning based computer vision
algorithms are applied to enable real-time on-board sensor processing for small
UAVs. Four use cases are considered: target detection, classification and
localization, road segmentation for autonomous navigation in GNSS-denied zones,
human body segmentation, and human action recognition. All algorithms have been
developed using state-of-the-art image processing methods based on deep neural
networks. Acquisition campaigns have been carried out to collect custom
datasets reflecting typical operational scenarios, where the peculiar point of
view of a multi-rotor UAV is replicated. Algorithms architectures and trained
models performances are reported, showing high levels of both accuracy and
inference speed. Output examples and on-field videos are presented,
demonstrating models operation when deployed on a GPU-powered commercial
embedded device (NVIDIA Jetson Xavier) mounted on board of a custom quad-rotor,
paving the way to enabling high level autonomy.
