Using the CGAN Model Extend Encounter Targets Image Training Set

Ruolan Zhang, Masao Furusho

Abstract

A fully capable unmanned ship navigation requires full autonomous decision-making, large-scale decision model training data to answer for these conditions is essential. However, it is difficult to obtain enough scenes training data in a real sea navigation environment. In response to possible emergency situations even no shore-station support, this paper proposes a method using conditional generative adversarial networks (CGAN) to generate the most executable large-scale target ships image set, which can be used to training various sea conditions autonomous decision-making model. In practice, most of the current research on unmanned ships are based onshore remote control or monitoring. Nonetheless, in some extremely special circumstances, such as communication interruption, or if the ship cannot be guided or remotely controlled in real time on the shore, the unmanned ship must make an appropriate decision and form new plans according to the encounter targets and the whole current situation. The CGAN model is a novel means to generate the target ships to construct the whole encounter sea scenes situation. The generated targets training image set can be used to train decision models, and explore a new way to approach large-scale, fully autonomous navigation decisions.

Download


Paper Citation


in Harvard Style

Zhang R. and Furusho M. (2019). Using the CGAN Model Extend Encounter Targets Image Training Set.In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-374-2, pages 327-332. DOI: 10.5220/0007676803270332


in Bibtex Style

@conference{vehits19,
author={Ruolan Zhang and Masao Furusho},
title={Using the CGAN Model Extend Encounter Targets Image Training Set},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2019},
pages={327-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007676803270332},
isbn={978-989-758-374-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Using the CGAN Model Extend Encounter Targets Image Training Set
SN - 978-989-758-374-2
AU - Zhang R.
AU - Furusho M.
PY - 2019
SP - 327
EP - 332
DO - 10.5220/0007676803270332