文章基本信息

标题：Underwater Object Detection and Pose Estimation using Deep Learning ⁎
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作者：MyungHwan Jeon ; Yeongjun Lee ; Young-Sik Shin 等
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.