Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of Ghost-DeblurGAN to Fiducial Marker Detection   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.

17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph