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

Christos Kyrkou; George Plastiras; Theocharis Theocharides; Stylianos I. Venieris; Christos-Savvas Bouganis


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  <dc:creator>Christos Kyrkou</dc:creator>
  <dc:creator>George Plastiras</dc:creator>
  <dc:creator>Theocharis Theocharides</dc:creator>
  <dc:creator>Stylianos I. Venieris</dc:creator>
  <dc:creator>Christos-Savvas Bouganis</dc:creator>
  <dc:date>2018-03-16</dc:date>
  <dc:description>Deep learning (DL) has gathered significant interest recently as an Artificial Intelligence (AI) paradigm, with success in a wide range of applications such as image and speech recognition, autonomous systems, self-driving cars, cyber-physical systems, and many more. Among the most promising systems that can utilize deep learning are Unmanned Aerial Vehicles (UAVs) which are becoming an attractive solution for a wide range of applications. In particular, Road Traffic Monitoring (RTM), and Emergency Response (ER) systems constitute a domain where the use of UAVs is receiving significant interest. Under the above deployments, UAVs are responsible for searching, collecting and sending, in real time, vehicle information either for traffic regulation purposes or to aid search and rescue in emergency response.</dc:description>
  <dc:description>© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works.

C. Kyrkou, G. Plastiras, T. Theocharides, S. I. Venieris and C. S. Bouganis, "DroNet: Efficient convolutional neural network detector for real-time UAV applications," 2018 Design, Automation &amp; Test in Europe Conference &amp; Exhibition (DATE), Dresden, Germany, 2018, pp. 967-972.
doi: 10.23919/DATE.2018.8342149


https://www.ieee.org/publications_standards/publications/rights/rights_policies.html

NVIDIA Corporation has supported this research with the donation of the Titan Xp GPU</dc:description>
  <dc:identifier>https://zenodo.org/record/1243708</dc:identifier>
  <dc:identifier>10.23919/DATE.2018.8342149</dc:identifier>
  <dc:identifier>oai:zenodo.org:1243708</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/739551/</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/ckyrkou</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/cyprus</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/kios-coe</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-sa/4.0/legalcode</dc:rights>
  <dc:subject>Detectors, Convolutional neural networks, Real-time systems, Training, Computer architecture, Machine learning, Graphics processing units</dc:subject>
  <dc:title>DroNet: Efficient convolutional neural network detector for real-time UAV applications</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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