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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    <subfield code="a">UAV  Deep Learning</subfield>
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    <subfield code="a">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.</subfield>
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    <subfield code="u">Pervasive Systems Group, Department of Computer Science University of Twente, The Netherlands</subfield>
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    <subfield code="u">Department of Computer Science, University of Cyprus, Nicosia, Cyprus</subfield>
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    <subfield code="a">Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles</subfield>
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    <subfield code="a">&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;</subfield>
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