Image-Based Monitoring of Jellyfish Using Deep Learning Architecture

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dc.contributor.authorKim, Hanguenko
dc.contributor.authorKoo, Jungmoko
dc.contributor.authorKim, Donghoonko
dc.contributor.authorJung, Sungwookko
dc.contributor.authorShin, Jae-Ukko
dc.contributor.authorLee, Serinko
dc.contributor.authorMyung, Hyunko
dc.date.accessioned2016-07-06T04:25:29Z-
dc.date.available2016-07-06T04:25:29Z-
dc.date.created2016-04-19-
dc.date.created2016-04-19-
dc.date.created2016-04-19-
dc.date.issued2016-04-
dc.identifier.citationIEEE SENSORS JOURNAL, v.16, no.8, pp.2215 - 2216-
dc.identifier.issn1530-437X-
dc.identifier.urihttp://hdl.handle.net/10203/209550-
dc.description.abstractJellyfish 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-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleImage-Based Monitoring of Jellyfish Using Deep Learning Architecture-
dc.typeArticle-
dc.identifier.wosid000372419100003-
dc.identifier.scopusid2-s2.0-84962124851-
dc.type.rimsART-
dc.citation.volume16-
dc.citation.issue8-
dc.citation.beginningpage2215-
dc.citation.endingpage2216-
dc.citation.publicationnameIEEE SENSORS JOURNAL-
dc.identifier.doi10.1109/JSEN.2016.2517823-
dc.contributor.localauthorMyung, Hyun-
dc.contributor.nonIdAuthorKoo, Jungmo-
dc.contributor.nonIdAuthorJung, Sungwook-
dc.contributor.nonIdAuthorLee, Serin-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorJellyfish monitoring-
dc.subject.keywordAuthorobject recognition-
dc.subject.keywordAuthorconvolutional neural network-
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