Application of Ghost-DeblurGAN to Fiducial Marker Detection

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. Detected markers are labeled by red frames. However, previous studies have not systems [10], [7], [9], [8] do not take motion blur as a routine dealt with the application of deep-learning-based deblurring case, which makes the adoption of the deblurring method methods in fiducial marker systems.