Image-Based Monitoring of Jellyfish Using Deep Learning Architecture

被引：30

作者：

Kim, Hanguen ^{[1
]}

Koo, Jungmo ^{[1
]}

Kim, Donghoon ^{[1
]}

Jung, Sungwook ^{[1
]}

Shin, Jae-Uk ^{[1
]}

Lee, Serin ^{[2
]}

Myung, Hyun ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Urban Robot Lab, Daejeon 34141, South Korea

[2] Inst Infocomm Res, Singapore 138632, Singapore

来源：

IEEE SENSORS JOURNAL | 2016年 / 16卷 / 08期

基金：

新加坡国家研究基金会;

关键词：

Jellyfish monitoring; object recognition; convolutional neural network;

D O I：

10.1109/JSEN.2016.2517823

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Jellyfish blooms have caused great damage to the fishery industry. In efforts to solve this problem, various systems to remove jellyfish have been proposed. This letter presents preliminary results of applying an image-based jellyfish distribution recognition algorithm to increase the efficiency of an existing jellyfish removal system. By using a convolutional neural network and dedicated image processing techniques, the experimental results show reasonable performance for real-world application.

引用

页码：2215 / 2216

页数：2

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