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

Andreas Kamilaris, Corjan van den Brink, Savvas Karatsiolis

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    3 Citations (Scopus)
    1 Downloads (Pure)

    Abstract

    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 classification problem of fire identification 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.
    Original languageEnglish
    Title of host publicationComputer Analysis of Images and Patterns
    Subtitle of host publicationCAIP 2019 International Workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019, Proceedings
    EditorsMario Vento, Gennaro Percannella, Sara Colantonio, Daniela Giorgi, Bogdan J. Matuszewski, Hamideh Kerdegari, Manzoor Razaak
    Place of PublicationCham
    PublisherSpringer
    Pages81-90
    ISBN (Electronic)978-3-030-29930-9
    ISBN (Print)978-3-030-29929-3
    DOIs
    Publication statusPublished - 23 Aug 2019
    EventWorkshop on Deep-Learning Based Computer Vision for UAV, DL-UAV19 - Grand Hotel Salerno, Salerno, Italy
    Duration: 6 Sep 20196 Sep 2019

    Publication series

    NameCommunications in Computer and Information Science
    PublisherSpringer
    Volume1089
    ISSN (Print)1865-0929
    ISSN (Electronic)1865-0937

    Workshop

    WorkshopWorkshop on Deep-Learning Based Computer Vision for UAV, DL-UAV19
    Abbreviated titleDL-UAV19
    CountryItaly
    CitySalerno
    Period6/09/196/09/19

    Keywords

    • UAV
    • Deep learning
    • Generative data
    • Aerial imagery

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