October 30, 2019 Conference paper Open Access

Kamilaris Andreas; van den Brink Corjan; Karatsiolis Savvas

MARC21 XML Export

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    <subfield code="a">UAV  Deep Learning</subfield>
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    <subfield code="a">This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement  No 739578 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="u">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</subfield>
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    <subfield code="a">Kamilaris Andreas</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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    <subfield code="t">Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089</subfield>
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