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Marine biodiversity for an inclusive society

2024-11-13Artigo científico Acesso aberto
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datacite.subject.sdg14:Proteger a Vida Marinha
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg13:Ação Climática
dc.contributor.authorVeiga, Ricardo
dc.contributor.authorRodrigues, Joao
dc.date.accessioned2026-04-07T09:04:21Z
dc.date.available2026-04-07T09:04:21Z
dc.date.issued2024-11-13
dc.description.abstractAn inclusive society actively seeks the equitable and respectful participation of all its members, regardless of their differences. This concept goes beyond tolerating diversity; it involves valuing and respecting each individual, ensuring everyone has equal access to information and opportunities, and actively participating in all aspects of life. To have a full inclusive society, we have to measure and monitor our impact in the environment, specifically in the oceans and marine life. This paper addresses this challenge by proposing a framework that leverages data aggregation and advanced machine learning techniques for Fine-Grained Visual Classification of marine species. Our methodology employs the Swin Transformer architecture, enhanced with the Fine-Grained Visual Classification Plug-in Module, to process and classify diverse marine datasets. We aggregated multiple marine datasets, preprocessed them to eliminate invalid entries, and trained our model on the refined dataset. Our findings demonstrate that dataset aggregation significantly enhances model accuracy and robustness, especially for large-scale models. Notably, the aggregated data model achieved 94.75% overall accuracy on a dataset comprising 2,548 classes and 391,374 images, compared to 85.93% on individual datasets like WildFish++.eng
dc.description.sponsorshipUIDP/04516/2020
dc.identifier.doi10.1145/3696593.3696637
dc.identifier.isbn979-8-4007-0729-2
dc.identifier.urihttp://hdl.handle.net/10400.1/28604
dc.language.isoeng
dc.peerreviewedyes
dc.publisherAssociation for Computing Machinery (ACM)
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.relation.ispartofProceedings of the 11th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectComputer vision
dc.subjectData aggregation
dc.subjectData fusion
dc.subjectFine-grained visual classification
dc.subjectSwin transformer
dc.titleMarine biodiversity for an inclusive societyeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/04516/2020
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.citation.conferenceDate2024
oaire.citation.conferencePlaceAbu Dhabi, United Arab Emirates
oaire.citation.endPage49
oaire.citation.startPage42
oaire.citation.titleDSAI '24: Proceedings of the 11th International Conference on Soware Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameVeiga
person.familyNameRodrigues
person.givenNameRicardo
person.givenNameJoao
person.identifier1603578
person.identifier.ciencia-idD212-85A6-C85A
person.identifier.ciencia-id8A19-98F7-9914
person.identifier.orcid0000-0002-7557-8304
person.identifier.orcid0000-0002-3562-6025
person.identifier.scopus-author-id57203130604
person.identifier.scopus-author-id55807461600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication60cb9fb6-94b2-4657-935e-3e7eea696a49
relation.isAuthorOfPublication683ba85b-459c-4789-a4ff-a4e2a904b295
relation.isAuthorOfPublication.latestForDiscovery60cb9fb6-94b2-4657-935e-3e7eea696a49
relation.isProjectOfPublication1122b3d4-9740-4ad7-9abf-86bb7a3615da
relation.isProjectOfPublication.latestForDiscovery1122b3d4-9740-4ad7-9abf-86bb7a3615da

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