Application of Ghost-DeblurGAN to Fiducial Marker Detection
Yibo Liu, Amaldev Haridevan, Hunter Schofield, Jinjun Shan
Abstract
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, York-Tag, 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.
BibTeX
@inproceedings{iros2022_applicationofgho,
title = {Application of Ghost-DeblurGAN to Fiducial Marker Detection},
author = {Yibo Liu and Amaldev Haridevan and Hunter Schofield and Jinjun Shan},
booktitle = {IROS 2022},
year = {2022}
}