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.
@article{arxiv.1908.06472,
title = {Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles},
author = {Andreas Kamilaris and Corjan van den Brink and Savvas Karatsiolis},
journal= {arXiv preprint arXiv:1908.06472},
year = {2019}
}
Comments
Workshop on Deep-learning based computer vision for UAV in conjunction with CAIP 2019, Salerno, italy, September 2019