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

Andreas Kamilaris, Corjan van den Brink, Savvas Karatsiolis

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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
PublisherArXiv.org
DOIs
Publication statusPublished - 18 Aug 2019

Keywords

  • cs.CV
  • cs.LG
  • eess.IV

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

    Kamilaris, A., van den Brink, C. & Karatsiolis, S., 23 Aug 2019, Computer Analysis of Images and Patterns : CAIP 2019 International Workshops, ViMaBi and DL-UAV, Salerno, Italy, September 6, 2019, Proceedings. Vento, M., Percannella, G., Colantonio, S., Giorgi, D., Matuszewski, B. J., Kerdegari, H. & Razaak, M. (eds.). Cham: Springer, p. 81-90 (Communications in Computer and Information Science; vol. 1089).

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