Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data

Mauren C. de Andrade, Matheus Nogueira, Eduardo Fidelis, Luiz Campos, Pietro Campos, Torsten Schön, Lester de Abreu Faria

2023

Abstract

In this paper, we evaluate the training of GAN for synthetic RAD image generation for four objects reflected by Frequency Modulated Continuous Wave radar: car, motorcycle, pedestrian and truck. This evaluation adds a new possibility for data augmentation when radar data labeling available is not enough. The results show that, yes, the GAN generated RAD images well, even when a specific class of the object is necessary. We also compared the scores of three GAN architectures, GAN Vanilla, CGAN, and DCGAN, in RAD synthetic imaging generation. We show that the generator can produce RAD images well enough with the results analyzed.

Paper Citation

in Harvard Style

C. de Andrade M., Nogueira M., Fidelis E., Campos L., Campos P., Schön T. and de Abreu Faria L. (2023). Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 262-271. DOI: 10.5220/0011672400003417

in Bibtex Style

@conference{visapp23,
author={Mauren C. de Andrade and Matheus Nogueira and Eduardo Fidelis and Luiz Campos and Pietro Campos and Torsten Schön and Lester de Abreu Faria},
title={Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={262-271},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011672400003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data
SN - 978-989-758-634-7
AU - C. de Andrade M.
AU - Nogueira M.
AU - Fidelis E.
AU - Campos L.
AU - Campos P.
AU - Schön T.
AU - de Abreu Faria L.
PY - 2023
SP - 262
EP - 271
DO - 10.5220/0011672400003417
PB - SciTePress