Paper Abstract and Keywords |
Presentation |
2017-01-26 09:25
Development of species classification system by the deep learning using the broadband split-beam Atsushi Kinjo, Ikuo Matsuo (TGU), Tomohito Imaizumi (FRA,NRIFE) US2016-108 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
pecies classification using an acoustic sounder is important for fisheries. With schools of mixed species, it is necessary to isolate individual fish echoes in order to classify individual fish species from echoes. A broadband signal, which offered the advantage of high-range resolution, was applied for this purpose, and the positions of fish were estimated using the split-beam system. The target strength (TS) spectra of individual fish echoes could be computed from isolated echoes and estimated positions. In this paper, we proposed the fish classification system by using these TS spectra. We examined two methods, which were Nearest Neighbor Algorithm (NNA) and Deep Learning as machine learning. Subject species were chub mackerel (Scomber japonicas), Japanese jack mackerel (Trachurus japonicus) and Japanese anchovy (Engraulis japonicas). It was found that the classification rates using the Deep Learning were superior to those using the NNA and the classification rate using Deep Learning was about 68%. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Machine Learning / Deep Learning / Fish classification / Split-Beam, / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 419, US2016-108, pp. 173-178, Jan. 2017. |
Paper # |
US2016-108 |
Date of Issue |
2017-01-18 (US) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and 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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US2016-108 |
Conference Information |
Committee |
EA US |
Conference Date |
2017-01-25 - 2017-01-26 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Doshisha Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
[Joint Meeting on Acoustics and Ultrasonics Subsociety] Engineering/Electro Acoustics, Ultrasonic and Underwater Acoustics, and Related Topics |
Paper Information |
Registration To |
US |
Conference Code |
2017-01-EA-US |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Development of species classification system by the deep learning using the broadband split-beam |
Sub Title (in English) |
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Machine Learning |
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Deep Learning |
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Fish classification |
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Split-Beam, |
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1st Author's Name |
Atsushi Kinjo |
1st Author's Affiliation |
Tohoku Gakuin University (TGU) |
2nd Author's Name |
Ikuo Matsuo |
2nd Author's Affiliation |
Tohoku Gakuin University (TGU) |
3rd Author's Name |
Tomohito Imaizumi |
3rd Author's Affiliation |
Fisheries Research Agency National Research Institute of Fisheries Engineering (FRA,NRIFE) |
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Speaker |
Author-1 |
Date Time |
2017-01-26 09:25:00 |
Presentation Time |
25 minutes |
Registration for |
US |
Paper # |
US2016-108 |
Volume (vol) |
vol.116 |
Number (no) |
no.419 |
Page |
pp.173-178 |
#Pages |
6 |
Date of Issue |
2017-01-18 (US) |
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