Conference paper Embargoed Access

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

Kamilaris Andreas; van den Brink Corjan; Karatsiolis Savvas


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        <foaf:name>van den Brink Corjan</foaf:name>
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        <foaf:name>Karatsiolis Savvas</foaf:name>
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    <dct:title>Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dcat:keyword>UAV Deep Learning</dcat:keyword>
    <dcat:keyword>Generative Data</dcat:keyword>
    <dcat:keyword>Aerial Imagery</dcat:keyword>
    <dct:available rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-10-30</dct:available>
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    <dct:description>&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;</dct:description>
    <dct:description xml:lang="">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.</dct:description>
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