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dc.contributor.authorPuig-Pons, V.
dc.contributor.authorMuñoz-Benavent, P.
dc.contributor.authorEspinosa, V.
dc.contributor.authorAndreu-García, G.
dc.contributor.authorValiente-González, J.M.
dc.contributor.authorEstruch, V.D.
dc.contributor.authorOrdóñez, P.
dc.contributor.authorPérez-Arjona, I.
dc.contributor.authorAtienza, V.
dc.contributor.authorMèlich, B.
dc.contributor.authorde-la-Gándara, F. (Fernando) 
dc.contributor.authorSantaella-Álvarez, E. (Eladio) 
dc.date.accessioned2020-06-23T08:45:47Z
dc.date.available2020-06-23T08:45:47Z
dc.date.issued2019
dc.identifier.issn01448609
dc.identifier.urihttp://hdl.handle.net/10508/11451
dc.description.abstractIn this work, acoustic and computer vision techniques are combined to develop an automatic procedure for biomass estimation of tuna during transfers. A side scan sonar working at 200 kHz and a stereo camera, posi- tioned facing towards the surface to record the ventral aspect of fish, are set as acquisition equipment. Moreover, a floating structure has been devised to place the sensors between cages in transfers, creating a transfer canal that allows data acquisition while fish swim from donor to receiving cage. Biomass assessment is computed by counting transferred tuna and sizing a representative sample of the stock. The number of transferred tuna is automatically deduced from acoustic echograms by means of image processing techniques, whereas tuna size is computed from the stereo videos using our automatic computer vision procedure based on a deformable model of the fish ventral silhouette. The results show that the system achieves automatic tuna counting with error below 10%, achieving around 1% error in the best configuration, and automatic tuna sizing of more than 20% of the stock, with highly accurate Snout Fork Length estimation when compared to true data from harvests. These results fulfil the requirements imposed by International Commission for the Conservation of Atlantic Tunas for compliant transfer operations.es_ES
dc.language.isoenges_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectatún rojoes_ES
dc.subjectbluefin tunaes_ES
dc.subjectThunnus thynnuses_ES
dc.subjectfish farminges_ES
dc.subjecttuna fatteninges_ES
dc.subjectBluefin Tuna biomass estimationes_ES
dc.subjectUnderwater acousticses_ES
dc.subjectUnderwater computer visiones_ES
dc.titleAutomatic Bluefin Tuna (Thunnus thynnus) Biomass Estimation during Transfers Using Acoustic and Computer Vision Techniqueses_ES
dc.typearticlees_ES
dc.identifier.bibliographicCitationAquacultural Engineering, 85. 2019: 22-31*
dc.description.versionVersión del editores_ES
dc.publisher.centreCentro Oceanográfico de Murciaes_ES
dc.relation.projectIDBIACOP project, ES13/41 y ACUSTUNA project ref. CTM2015-70446-Res_ES
dc.rights.accessRightsembargoedAccesses_ES
dc.description.impact1,421*
dc.identifier.doi10.1016/j.aquaeng.2019.01.005
dc.subject.asfafish es_ES
dc.subject.asfaengineering es_ES
dc.subject.asfavision es_ES
dc.subject.asfabiomass es_ES


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