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Paper Abstract and Keywords
Presentation 2018-10-11 14:30
A study on ship type identification by use of deep neural network
Ryouichi Nishimura, Katsuhiro Temma (NICT), Kiyohiko Hattori (Saitama Inst. of Tech.), Kenji Kaneko (TEAMS), Akinori Ito (Tohoku Univ.), Toyonobu Fujii (TEAMS), Akihiro Kijima (Tohoku Univ.) EA2018-54
Abstract (in Japanese) (See Japanese page) 
(in English) Poaching has recently become a serious problem due to the globalization of food culture and the accompanied rising prices. Considering bad effects on the global ecosystems by the overfishing, potential deficit would be enormous in the future. Workers on aquaculture are now forced to keep surveying a poaching boat throughout the night because such a boat should be caught in flagrante delicto. If a machine can do the job instead of humans, it would be a promising solution. Poaching boats usually move around in the dark. Therefore, it is desirable to detect them using characteristics of the sound, namely audio fingerprint, produced by each boat. We tried to develop such a system using Deep Neural Network (DNN). Sound and video of ships were continuously recorded and databases were constructed using the data for 26 days. Classification into 14 classes combined with a post process of moving average and binarization showed a performance of approximately 0.9 at most in F-measure depending on ship types.
Keyword (in Japanese) (See Japanese page) 
(in English) Aquaculture / Poaching surveillance / Cepstrum / Real-time system / DNN / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 234, EA2018-54, pp. 1-6, Oct. 2018.
Paper # EA2018-54 
Date of Issue 2018-10-04 (EA) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee EA  
Conference Date 2018-10-11 - 2018-10-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Iwaki business Innovation Center (Iwaki) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Engineering/Electro Acoustics, and Related Topics 
Paper Information
Registration To EA 
Conference Code 2018-10-EA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on ship type identification by use of deep neural network 
Sub Title (in English)  
Keyword(1) Aquaculture  
Keyword(2) Poaching surveillance  
Keyword(3) Cepstrum  
Keyword(4) Real-time system  
Keyword(5) DNN  
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1st Author's Name Ryouichi Nishimura  
1st Author's Affiliation National Institute of Information and Communications Technology (NICT)
2nd Author's Name Katsuhiro Temma  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
3rd Author's Name Kiyohiko Hattori  
3rd Author's Affiliation Saitama Institute of Technology (Saitama Inst. of Tech.)
4th Author's Name Kenji Kaneko  
4th Author's Affiliation Tohoku Ecosystem-Associated Marine Sciences (TEAMS)
5th Author's Name Akinori Ito  
5th Author's Affiliation Tohoku University (Tohoku Univ.)
6th Author's Name Toyonobu Fujii  
6th Author's Affiliation Tohoku Ecosystem-Associated Marine Sciences (TEAMS)
7th Author's Name Akihiro Kijima  
7th Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker Author-1 
Date Time 2018-10-11 14:30:00 
Presentation Time 25 minutes 
Registration for EA 
Paper # EA2018-54 
Volume (vol) vol.118 
Number (no) no.234 
Page pp.1-6 
#Pages
Date of Issue 2018-10-04 (EA) 


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