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  • 标题:Underwater Object Detection and Pose Estimation using Deep Learning ⁎
  • 本地全文:下载
  • 作者:MyungHwan Jeon ; Yeongjun Lee ; Young-Sik Shin
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:21
  • 页码:78-81
  • DOI:10.1016/j.ifacol.2019.12.286
  • 语种:English
  • 出版社:Elsevier
  • 摘要:This paper presents an approach for making a dataset using a 3D CAD model for deep learning based underwater object detection and pose estimation. We also introduce a simple pose estimation network for underwater objects. In the experiment, we show that object detection and pose estimation networks trained via our synthetic dataset present a preliminary potential for deep learning based approaches in underwater. Lastly, we show that our synthetic image dataset provides meaningful performance for deep learning models in underwater environments.
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