Feature extraction or localization based on the fiducial marker could fail due to motion blur in real-world robotic applications. To solve this problem, a lightweight generative adversarial network, named Ghost-DeblurGAN, for real-time motion deblurring is developed in this paper. Furthermore, on account that there is no existing deblurring benchmark for such task, a new large-scale dataset, YorkTag, is proposed that provides pairs of sharp/blurred images containing fiducial markers. With the proposed model trained and tested on YorkTag, it is demonstrated that when applied along with fiducial marker systems to motion-blurred images, Ghost-DeblurGAN improves the marker detection significantly. The datasets and codes used in this paper are available at: https://github.com/York-SDCNLab/Ghost-DeblurGAN.
@article{arxiv.2109.03379,
title = {Application of Ghost-DeblurGAN to Fiducial Marker Detection},
author = {Yibo Liu and Amaldev Haridevan and Hunter Schofield and Jinjun Shan},
journal= {arXiv preprint arXiv:2109.03379},
year = {2023}
}