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.
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Notes
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Python Imaging Library. https://pypi.python.org/pypi/PIL.
- 2.
- 3.
Open AI Tanzania Challenge. https://blog.werobotics.org/2018/08/06/welcome-to-the-open-ai-tanzania-challenge/.
- 4.
Unity. https://unity.com.
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Kamilaris, A., van den Brink, C., Karatsiolis, S. (2019). Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles. In: Vento, M., et al. Computer Analysis of Images and Patterns. CAIP 2019. Communications in Computer and Information Science, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-030-29930-9_8
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