Conference paper Open 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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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Kamilaris Andreas</dc:creator>
  <dc:creator>van den Brink Corjan</dc:creator>
  <dc:creator>Karatsiolis Savvas</dc:creator>
  <dc:description>This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the
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.</dc:description>
  <dc:description>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.</dc:description>
  <dc:publisher>Springer International Publishing</dc:publisher>
  <dc:subject>UAV  Deep Learning</dc:subject>
  <dc:subject>Generative Data</dc:subject>
  <dc:subject>Aerial Imagery</dc:subject>
  <dc:title>Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles</dc:title>
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