Show simple item record

dc.contributor.authorLekunberri, Xabier
dc.contributor.authorRuiz, Jon
dc.contributor.authorQuincoces, Iñaki
dc.contributor.authorDornaika, Fadi ORCID
dc.contributor.authorArganda Carreras, Ignacio
dc.contributor.authorFernandes, Jose A.
dc.identifier.citationEcological Informatics 67 : (2022) // Article ID 101495es_ES
dc.description.abstractFishery monitoring programs are essential for effective management of marine resources, as they provide scientists and managers with the necessary data for both the preparation of scientific advice and fisheries control and surveillance. The monitoring is generally done by human observers, both in port and onboard, with a high cost involved. Consequently, some Regional Fisheries Management Organizations (RFMO) are opting for electronic monitoring (EM) as an alternative or complement to human observers in certain fisheries. This is the case of the tropical tuna purse seine fishery operating in the Indian and Atlantic oceans, which started an EM program on a voluntary basis in 2017. However, even when the monitoring is conducted though EM, the image analysis is a tedious task manually performed by experts. In this paper, we propose a cost-effective methodology for the automatic processing of the images already being collected by cameras onboard tropical tuna purse seiners. Firstly, the images are preprocessed to homogenize them across all vessels and facilitate subsequent steps. Secondly, the fish are individually segmented using a deep neural network (Mask R-CNN). Then, all segments are passed through other deep neural network (ResNet50V2) to classify them by species and estimate their size distribution. For the classification of fish, we achieved an accuracy for all species of over 70%, i.e., about 3 out of 4 individuals are correctly classified to their corresponding species. The size distribution estimates are aligned with official port measurements but calculated using a larger number of individuals. Finally, we also propose improvements to the current image capture systems which can facilitate the work of the proposed automation methodology.es_ES
dc.description.sponsorshipThis project is funded by the Basque Government, and the Spanish fisheries ministry through the EU next Generation funds. Jose A. Fernandes'work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreements No 869342 (SusTunTech). This work is supported in part by the University of the Basque Country UPV/EHU grant GIU19/027. We want to thank the expert analysts who helped to annotate images with incredible effort: Manuel Santos and Inigo Krug. We also like to extend our gratitude to Marine Instruments for providing the necessary equipment tocollect the data. This paper is contribution no 1080 from AZTI, Marine Research, Basque Research and Technology Alliance (BRTA) .es_ES
dc.subjectcomputer visiones_ES
dc.subjectdeep learninges_ES
dc.subjectelectronic monitoringes_ES
dc.subjecttropical tuna species identificationes_ES
dc.titleIdentification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learninges_ES
dc.rights.holder© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.es_ES
dc.rights.holderAtribución-NoComercial-SinDerivadas 3.0 España*
dc.contributor.funderEuropean Commission
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES

Files in this item


This item appears in the following Collection(s)

Show simple item record

© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
Except where otherwise noted, this item's license is described as © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.