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http://hdl.handle.net/10261/223605
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Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | Martín-Abadal, Miguel | es_ES |
dc.contributor.author | Ruiz-Frau, Ana | es_ES |
dc.contributor.author | Hinz, Hilmar | es_ES |
dc.contributor.author | González-Cid, Yolanda | es_ES |
dc.date.accessioned | 2020-11-25T07:41:27Z | - |
dc.date.available | 2020-11-25T07:41:27Z | - |
dc.date.issued | 2020-03-19 | - |
dc.identifier.citation | Sensors 20(6): 1708 (2020) | es_ES |
dc.identifier.uri | http://hdl.handle.net/10261/223605 | - |
dc.description.abstract | During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans. | es_ES |
dc.description.sponsorship | Miguel Martin-Abadal was supported by Ministry of Economy and Competitiveness (AEI,FEDER,UE), under contract DPI2017-86372-C3-3-R. Ana Ruiz-Frau was supported by a Marie-Sklodowska-Curie Individual Fellowship (JellyPacts project number 655475). Hilmar Hinz was supported through a Ramón y Cajal Fellowship financed by the Ministerio de Economía y Competitividad de España and the Conselleria d’Educació, Cultura i Universitats Comunidad Autónoma de las Islas Baleares (RyC 2013 14729). Yolanda Gonzalez-Cid was supported by Ministry of Economy and Competitiveness (AEI,FEDER,UE), under contracts TIN2017-85572-P and DPI2017-86372-C3-1-R. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Multidisciplinary Digital Publishing Institute | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86372-C3-3-R | es_ES |
dc.relation | DPI2017-86372-C3-3-R/AEI/10.13039/501100011033 | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/655475 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RYC-2013-14729 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TIN2017-85572-P | es_ES |
dc.relation | TIN2017-85572-P/AEI/10.13039/501100011033 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86372-C3-1-R | es_ES |
dc.relation | DPI2017-86372-C3-1-R/AEI/10.13039/501100011033 | es_ES |
dc.relation.isversionof | Publisher's version | es_ES |
dc.rights | openAccess | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Object detection | es_ES |
dc.subject | Jellyfish quantification | es_ES |
dc.subject | Jellyfish monitoring | es_ES |
dc.title | Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection | es_ES |
dc.type | artículo | es_ES |
dc.identifier.doi | 10.3390/s20061708 | - |
dc.description.peerreviewed | Peer reviewed | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s20061708 | es_ES |
dc.identifier.e-issn | 1424-8220 | - |
dc.rights.license | http://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es_ES |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (España) | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación (España) | es_ES |
dc.contributor.funder | Govern de les Illes Balears | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.relation.csic | Sí | es_ES |
oprm.item.hasRevision | no ko 0 false | * |
dc.identifier.funder | http://dx.doi.org/10.13039/501100000780 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100011033 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100003329 | es_ES |
dc.identifier.pmid | 32204330 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.openairetype | artículo | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
Aparece en las colecciones: | (IMEDEA) Artículos |
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sensors-20-01708.pdf | 4,79 MB | Adobe PDF | Visualizar/Abrir |
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