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dc.contributor.authorUranga, Jon
dc.contributor.authorArrizabalaga de Mingo, Haritz
dc.contributor.authorBoyra Eizaguirre, Guillermo
dc.contributor.authorHernández Gómez, María del Carmen ORCID
dc.contributor.authorGoñi, Nicolás
dc.contributor.authorArregui, Igor
dc.contributor.authorFernandes Salvador, Jose Antonio
dc.contributor.authorYurramendi Mendizabal, Yosu
dc.contributor.authorSantiago, Josu
dc.date.accessioned2019-04-30T09:18:44Z
dc.date.available2019-04-30T09:18:44Z
dc.date.issued2017-02-02
dc.identifier.citationPLOS ONE 12(2) : (2017) // Article ID e0171382es_ES
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10810/32586
dc.description.abstractThis study presents a methodology for the automated analysis of commercial medium range sonar signals for detecting presence/absence of bluefin tuna (Tunnus thynnus) in the Bay of Biscay. The approach uses image processing techniques to analyze sonar screen shots. For each sonar image we extracted measurable regions and analyzed their characteristics. Scientific data was used to classify each region into a class ("tuna" or "no-tuna") and build a dataset to train and evaluate classification models by using supervised learning. The methodology performed well when validated with commercial sonar screenshots, and has the potential to automatically analyze high volumes of data at a low cost. This represents a first milestone towards the development of acoustic, fishery-independent indices of abundance for bluefin tuna in the Bay of Biscay. Future research lines and additional alternatives to inform stock assessments are also discussed.es_ES
dc.description.sponsorshipThis research was supported by the Basque Government through PhD grant 0033-2011 to JU and grant GV 351NPVA00062 to HA (AZTI-Tecnalia). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.es_ES
dc.language.isoenges_ES
dc.publisherPublic Library Sciencees_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectthunnus-thynnuses_ES
dc.subjectfisheries researches_ES
dc.subjectstock assessmentes_ES
dc.subjectunit effortes_ES
dc.subjectatlantices_ES
dc.subjectschoolses_ES
dc.subjectmanagementes_ES
dc.subjectbiscayes_ES
dc.subjectbayes_ES
dc.subjectclassificationes_ES
dc.titleDetecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vesselses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2017 Uranga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171382es_ES
dc.identifier.doi10.1371/journal.pone.0171382
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


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© 2017 Uranga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as © 2017 Uranga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.