dc.contributor.author | Kyrkou, Christos | en |
dc.contributor.author | Plastiras, George | en |
dc.contributor.author | Theocharides, Theocharis | en |
dc.contributor.author | Venieris, Stylianos I. | en |
dc.contributor.author | Bouganis, Christos-Savvas | en |
dc.creator | Kyrkou, Christos | en |
dc.creator | Plastiras, George | en |
dc.creator | Theocharides, Theocharis | en |
dc.creator | Venieris, Stylianos I. | en |
dc.creator | Bouganis, Christos-Savvas | en |
dc.date.accessioned | 2021-01-26T09:45:46Z | |
dc.date.available | 2021-01-26T09:45:46Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/63391 | |
dc.description.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 | en |
dc.description.abstract | 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. | en |
dc.source | 2018 Design, Automation Test in Europe Conference Exhibition (DATE) | en |
dc.title | DroNet: Efficient convolutional neural network detector for real-time UAV applications | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.23919/DATE.2018.8342149 | |
dc.description.startingpage | 967 | |
dc.description.endingpage | 972 | |
dc.author.faculty | Πολυτεχνική Σχολή / Faculty of Engineering | |
dc.author.department | Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering | |
dc.type.uhtype | Conference Object | en |
dc.contributor.orcid | Kyrkou, Christos [0000-0002-7926-7642] | |
dc.contributor.orcid | Theocharides, Theocharis [0000-0001-7222-9152] | |
dc.contributor.orcid | Venieris, Stylianos I. [0000-0001-5181-6251] | |
dc.contributor.orcid | Bouganis, Christos-Savvas [0000-0002-4906-4510] | |
dc.gnosis.orcid | 0000-0002-7926-7642 | |
dc.gnosis.orcid | 0000-0001-7222-9152 | |
dc.gnosis.orcid | 0000-0001-5181-6251|0000-0002-4906-4510 | |