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
相关论文
共 50 条
  • [1] Planar Pose Estimation Using Object Detection and Reinforcement Learning
    Rasmussen, Frederik Norby
    Andersen, Sebastian Terp
    Grossmann, Bjarne
    Boukas, Evangelos
    Nalpantidis, Lazaros
    [J]. COMPUTER VISION SYSTEMS (ICVS 2019), 2019, 11754 : 353 - 365
  • [2] Real-time pose estimation for an underwater object combined with deep learning and prior information
    Ge, Xianwei
    Chi, Shukai
    Jia, Wei
    Jiang, Ke
    [J]. APPLIED OPTICS, 2022, 61 (24) : 7108 - 7118
  • [3] Joint Learning of Object Detection and Pose Estimation using Augmented Autoencoder
    Hayashi, Ryota
    Shimokura, Asei
    Matsumoto, Takuya
    Ukita, Norimichi
    [J]. PROCEEDINGS OF 17TH INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS (MVA 2021), 2021,
  • [4] Object Recognition and Pose Estimation base on Deep Learning
    Xue, Li-wei
    Chen, Li-guo
    Liu, Ji-zhu
    Wang, Yang-jun
    Shen, Qi
    Huang, Hai-bo
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (IEEE ROBIO 2017), 2017, : 1288 - 1293
  • [5] Study of Underwater Fruit Object Detection Using Deep Learning Model
    Aravind, Jinka Venkata
    Prince, Shanthi
    [J]. OPTICAL AND WIRELESS TECHNOLOGIES, OWT 2021, 2023, 892 : 381 - 391
  • [6] Minimizing the Effect of Specular Reflection on Object Detection and Pose Estimation of Bin Picking Systems Using Deep Learning
    Jayasinghe, Daksith
    Abeysinghe, Chandima
    Opanayaka, Ramitha
    Dinalankara, Randima
    Silva, Bhagya Nathali
    Wijesinghe, Ruchire Eranga
    Wijenayake, Udaya
    [J]. MACHINES, 2023, 11 (01)
  • [7] Object pose estimation in underwater acoustic images
    Murino, V
    Foresti, GL
    Trucco, A
    [J]. INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL I, 1997, : 873 - 876
  • [8] Deep Learning on Underwater Marine Object Detection: A Survey
    Moniruzzaman, Md.
    Islam, Syed Mohammed Shamsul
    Bennamoun, Mohammed
    Lavery, Paul
    [J]. ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS (ACIVS 2017), 2017, 10617 : 150 - 160
  • [9] Realistic Sonar Image Simulation Using Deep Learning for Underwater Object Detection
    Minsung Sung
    Jason Kim
    Meungsuk Lee
    Byeongjin Kim
    Taesik Kim
    Juhwan Kim
    Son-Cheol Yu
    [J]. International Journal of Control, Automation and Systems, 2020, 18 : 523 - 534
  • [10] Realistic Sonar Image Simulation Using Deep Learning for Underwater Object Detection
    Sung, Minsung
    Kim, Jason
    Lee, Meungsuk
    Kim, Byeongjin
    Kim, Taesik
    Kim, Juhwan
    Yu, Son-Cheol
    [J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2020, 18 (03) : 523 - 534