Published October 30, 2019
| Version v1
Conference paper
Open
Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles
- 1. 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
- 2. Pervasive Systems Group, Department of Computer Science University of Twente, The Netherlands
- 3. Department of Computer Science, University of Cyprus, Nicosia, Cyprus
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.
Notes
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.
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