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

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 describ...

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Bibliographic Details
Published in:arXiv.org 2019-08
Main Authors: Kamilaris, Andreas, Corjan van den Brink, Karatsiolis, Savvas
Format: Article
Language:English
Subjects:
Deep learning
Imagery
Machine learning
Synthetic data
Training
Unmanned aerial vehicles
Urban areas
Online Access:Get full text
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Summary: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.
ISSN:2331-8422