Name
Title | ||
|---|---|---|
Dronet: Efficient Convolutional Neural Network Detector For Real-Time Uav Applications |
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. |
Year | Venue | DocType |
|---|---|---|
2018 | PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) | Journal |
Volume | ISSN | Citations |
abs/1807.06789 | 1530-1591 | 1 |
PageRank | References | Authors |
0.44 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christos Kyrkou | 1 | 102 | 14.05 |
| George Plastiras | 2 | 17 | 2.30 |
| Theo Theocharides | 3 | 7 | 2.75 |
| Stylianos I. Venieris | 4 | 106 | 12.98 |
| Christos-Savvas Bouganis | 5 | 37 | 7.60 |