Conference paper Open Access

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

Kamilaris Andreas; van den Brink Corjan; Karatsiolis Savvas


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">UAV  Deep Learning</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Generative Data</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Aerial Imagery</subfield>
  </datafield>
  <controlfield tag="005">20200730130602.0</controlfield>
  <datafield tag="500" ind1=" " ind2=" ">
    <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>
  </datafield>
  <controlfield tag="001">3523006</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Pervasive Systems Group, Department of Computer Science University of Twente, The Netherlands</subfield>
    <subfield code="a">van den Brink Corjan</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Department of Computer Science, University of Cyprus, Nicosia, Cyprus</subfield>
    <subfield code="a">Karatsiolis Savvas</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">1298522</subfield>
    <subfield code="z">md5:2e8ab4fd5be9f9721d9967a058e66477</subfield>
    <subfield code="u">https://zenodo.org/record/3523006/files/pre_print.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2019-10-30</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-rise-teaming-cyprus</subfield>
    <subfield code="o">oai:zenodo.org:3523006</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <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>
    <subfield code="0">(orcid)0000-0002-8484-4256</subfield>
    <subfield code="a">Kamilaris Andreas</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-rise-teaming-cyprus</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">739578</subfield>
    <subfield code="a">Research Center on Interactive Media, Smart System and Emerging Technologies</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <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>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.3523005</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="g">81-90</subfield>
    <subfield code="b">Springer International Publishing</subfield>
    <subfield code="a">Cham</subfield>
    <subfield code="t">Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3523006</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
  </datafield>
</record>
70
24
views
downloads
All versions This version
Views 7070
Downloads 2424
Data volume 31.2 MB31.2 MB
Unique views 6161
Unique downloads 2222

Share

Cite as