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Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles

Kamilaris Andreas; van den Brink Corjan; Karatsiolis Savvas

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  <identifier identifierType="DOI">10.5281/zenodo.3523006</identifier>
      <creatorName>Kamilaris Andreas</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-8484-4256</nameIdentifier>
      <affiliation>Pervasive Systems Group, Department of Computer Science University of Twente, The Netherlands,Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE), Nicosia, Cyprus</affiliation>
      <creatorName>van den Brink Corjan</creatorName>
      <affiliation>Pervasive Systems Group, Department of Computer Science University of Twente, The Netherlands</affiliation>
      <creatorName>Karatsiolis Savvas</creatorName>
      <affiliation>Department of Computer Science, University of Cyprus, Nicosia, Cyprus</affiliation>
    <title>Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles</title>
    <subject>UAV  Deep Learning</subject>
    <subject>Generative Data</subject>
    <subject>Aerial Imagery</subject>
    <date dateType="Available">2020-10-30</date>
    <date dateType="Accepted">2019-10-30</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3523005</relatedIdentifier>
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    <rights rightsURI="">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/embargoedAccess">Embargoed Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the&lt;br&gt;
learning procedure of deep learning (DL) models, with a focus on aerial photos produced by unmanned aerial vehicles (UAV). The general concept and methodology are described, and preliminary results are presented, based on a classication problem of re identication in forests as well as a counting problem of estimating number of houses in urban areas. The proposed technique constitutes a new possibility for the DL community, especially related to UAV-based imagery analysis, with much potential, promising results, and unexplored ground for further research.&lt;/p&gt;</description>
    <description descriptionType="Other">This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.</description>
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