Recurrent Motion Neural Network for Low Resolution Drone Detection

Bernard JE Evans, Hamish Christopher Pratt, Ian Reid, Thomas James Rowntree, Steven D Wiederman

Abstract:

Drones are becoming increasingly prevalent in everyday usage with many commercial applications in fields such
as construction work and agricultural surveying. Despite their
common commercial use, drones have been recently used with
malicious intent, such as airline disruptions at Gatwick Airport.
With the emerging issue of safety concerns for the public and
other airspace users, detecting and monitoring active drones in an
area is crucial. This paper introduces a recurrent convolutional
neural network (CNN) specifically designed for drone detection.
This CNN can detect drones from down-sampled images by
exploiting the temporal information of drones in flight and
outperforms a state-of-the-art conventional object detector. Due
to the lightweight and low resolution nature of this network, it
can be moun