@inproceedings{deAndradeVellosoNogueiraFidelisetal.2023, author = {de Andrade, Mauren Louise S. C. and Velloso Nogueira, Matheus and Fidelis, Eduardo and Aguiar Campos, Luiz Henrique and Campos, Pietro and Sch{\"o}n, Torsten and de Abreu Faria, Lester}, 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 - Volume 4}, editor = {Radeva, Petia and Farinella, Giovanni Maria and Bouatouch, Kadi}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-634-7}, doi = {https://doi.org/10.5220/0011672400003417}, pages = {262 -- 271}, year = {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.}, language = {en} }