Experimental assessment of vision-based sensing for small UAS sense and avoid

This paper presents first results of an experimental flight-test campaign aimed to gather data for performance assessment of non-cooperative Sense and Avoid architectures for small Unmanned Aircraft Systems (UAS). The attention is here focused on vision-based approaches. An innovative sensing technique is proposed which exploits a Deep Learning (DL) network as the main processing block of the detector algorithm, and a multi-temporal strategy for track generation and confirmation. Both the detection and tracking phases foresee adhoc solutions to deal with the presence of intruders either above or below the horizon. Two customized small quadcopters, equipped with high-resolution color cameras, are used to reproduce in flight low-altitude, near-collision scenarios characterized by different speed and height above ground, thus being able to act simultaneously as ownship and intruder. Results demonstrate the capability of the DL-based detector to provide maximum declaration range around 300 m and 100 m, above and below the horizon, respectively. The tracker can robustly produce firm track of the intruder while rejecting many false positives, particularly occurring in below-the horizon scenarios