Underwater Object Detection and Pose Estimation using Deep Learning

被引:11
|
作者
Jeon, MyungHwan [1 ]
Lee, Yeongjun [2 ]
Shin, Young-Sik
Jang, Hyesu
Kim, Ayoung [3 ]
机构
[1] Korea Adv Inst Sci & Technol, Dept Robot Program, Daejeon, South Korea
[2] KRISO, Daejeon, South Korea
[3] Korea Adv Inst Sci & Technol, Dept Civil & Environm Engn, Daejeon, South Korea
来源
IFAC PAPERSONLINE | 2019年 / 52卷 / 21期
关键词
D O I
10.1016/j.ifacol.2019.12.286
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:78 / 81
页数:4
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