DroNet: Efficient Convolutional Neural Network Detector for Real‐Time UAV Applications
Christos Kyrkou1,a, George Plastiras1,b, Theocharis Theocharides1,c , Stylianos I. Venieris2,d and Christos-Savvas Bouganis2,e
1KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
akyrkou.christos@ucy.ac.cy
bgplast01@ucy.ac.cy
cttheocharides@ucy.ac.cy
2Imperial College London
dstylianos.venieris10@imperial.ac.uk
echristos-savvas.bouganis@imperial.ac.uk
ABSTRACT
Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of computer vision algorithms in order to analyse the information captured from an on-board camera. Such applications include detecting vehicles for emergency response and traffic monitoring. This paper therefore, explores the trade‐offs involved in the development of a single‐shot object detector based on deep convolutional neural networks (CNNs) that can enable UAVs to perform vehicle detection under a resource constrained environment such as in a UAV. The paper presents a holistic approach for designing such systems; the data collection and training stages, the CNN architecture, and the optimizations necessary to efficiently map such a CNN on a lightweight embedded processing platform suitable for deployment on UAVs. Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames per‐ second for a variety of platforms with an overall accuracy of ∼ 95%. Overall, the proposed architecture is suitable for UAV applications, utilizing low‐power embedded processors that can be deployed on commercial UAVs.
