DroNet: Efficient convolutional neural network detector for real-time UAV applications

• Published in: 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) pp. 967 - 972
• Main Authors: Kyrkou, Christos, Plastiras, George, Theocharides, Theocharis, Venieris, Stylianos I., Bouganis, Christos-Savvas
• Format: Conference Proceeding
• Language: English
• Published: EDAA 01.03.2018
• Subjects: Computer architecture; Convolutional neural networks; Detectors; Graphics processing units; Machine learning; Real-time systems; Training
• ISSN: 1558-1101
• DOI: 10.23919/DATE.2018.8342149

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.