mlsanityData to 7 Oct 2026

Application of Ghost-DeblurGAN to Fiducial Marker Detection

arXiv 2109.03379 ↗First posted 8 Sep 2021eess.IV cs.AI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

22 of 35 references.

References · 22

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10 more
  1. 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 361
  2. 2021HINet: Half Instance Normalization Network for Image Restorationcited by 236
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  10. 2021Navigation of a Self-Driving Vehicle Using One Fiducial Markercited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Deep learning in motion deblurring: current status, benchmarks and future prospectscited by 5
  2. 2025HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimationcited by 4
  3. 2024L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registrationcited by 2
  4. 2026SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policycited by 0