Ghost-DeblurGAN在基准标记检测中的应用
Application of Ghost-DeblurGAN to Fiducial Marker Detection
AI总结：
本文提出轻量级生成对抗网络Ghost-DeblurGAN和新数据集YorkTag，解决机器人应用中运动模糊导致基准标记检测失败的问题，显著提升检测效果。
摘要：
在真实世界的机器人应用中，基于基准标记的特征提取或定位可能会因运动模糊而失败。为解决这一问题，本文开发了一种用于实时运动去模糊的轻量级生成对抗网络，名为Ghost-DeblurGAN。此外，由于目前没有针对此类任务的现有去模糊基准，本文提出了一个新的大规模数据集YorkTag，提供包含基准标记的清晰/模糊图像对。通过在YorkTag上训练和测试所提出的模型，结果表明，当Ghost-DeblurGAN与基准标记系统一起应用于运动模糊图像时，显著提升了标记检测效果。本文使用的数据集和代码可在以下网址获取：https://github.com/York-SDCNLab/Ghost-DeblurGAN。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
