Ghost-DeblurGANによるフィデューシャルマーカー検出の改善
Application of Ghost-DeblurGAN to Fiducial Marker Detection
軽量GAN「Ghost-DeblurGAN」でリアルタイムにモーションブラーを除去し、フィデューシャルマーカー検出精度を向上させた研究。大規模データセットYorkTagも新たに構築した。
著者: Yibo Liu, Amaldev Haridevan, Hunter Schofield, Jinjun Shan
分類: eess.IV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
