Recurrent Motion Neural Network for Low Resolution Drone Detection

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 mounted on a small processor and run at near real-time speeds.