No abstract

Commercial Unmanned aerial vehicle (UAV) industry, which is publicly known as drone, has seen a tremendous increase in last few years, making these devices highly accessible to public. This phenomenon has immediately raised security concerns due to fact that these devices can intentionally or unintentionally cause serious hazards. In order to protect critical locations, the academia and industry have proposed several solutions in recent years. Computer vision is extensively used to detect drones autonomously compared to other proposed solutions such as RADAR, acoustics and RF signal analysis thanks to its robustness. Among these computer vision-based approaches, we see the preference of deep learning algorithms thanks to their effectiveness. In this paper, we are presenting an autonomous drone detection and tracking system which uses a static wide-angle camera and a lower-angle camera mounted on a rotating turret. In order to use memory and time efficiently, we propose a combined multi-frame deep learning detection technique, where the frame coming from the zoomed camera on the turret is overlaid on the wide-angle static camera's frame. With this approach, we are able to build an efficient pipeline where the initial detection of small sized aerial intruders on the main image plane and their detection on the zoomed image plane is performed simultaneously, minimizing the cost of resource exhaustive detection algorithm. In addition to this, we present the integral system including tracking algorithms, deep learning classification architectures and the protocols.

No abstract

In order to improve our understanding of landing on small bodies and of asteroid evolution, we use our novel drop tower facility (Sunday et al. 2016) to perform lowvelocity (2 -40 cm/s), shallow impact experiments of a 10 cm diameter aluminum sphere into quartz sand in low effective gravities (∼ 0.2 − 1 m/s 2 ). Using in-situ accelerometers we measure the acceleration profile during the impacts and determine the peak accelerations, collision durations and maximum penetration depth. We find that the penetration depth scales linearly with the collision velocity but is independent of the effective gravity for the experimental range tested, and that the collision duration is independent of both the effective gravity and the collision velocity. No rebounds are observed in any of the experiments. Our low-gravity experimental results indicate that the transition from the quasi-static regime to the inertial regime occurs for impact energies two orders of magnitude smaller than in similar impact experiments under terrestrial gravity. The lower energy regime change may be due to the increased hydrodynamic drag of the surface material in our experiments, but may also support the notion that the quasi-static regime reduces as the effective gravity becomes lower.