Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

Corrección de una afiliación en Sensors 2023, 23, 16. https://doi.org/10.3390/s23010016

Detalles Bibliográficos
Autores: López Vázquez, Vanesa, López Guede, José Manuel, Marini, Simone, Fanelli, Emanuela, Johnsen, Espen, Aguzzi, Jacopo
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/59216
Acceso en línea:http://hdl.handle.net/10810/59216
Access Level:acceso abierto
Palabra clave:cabled observatories
artificial intelligence
deep learning
machine learning
deep-sea fauna
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spelling Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled ObservatoriesLópez Vázquez, VanesaLópez Guede, José ManuelMarini, SimoneFanelli, EmanuelaJohnsen, EspenAguzzi, Jacopocabled observatoriesartificial intelligencedeep learningmachine learningdeep-sea faunaCorrección de una afiliación en Sensors 2023, 23, 16. https://doi.org/10.3390/s23010016An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.This work was developed within the framework of the Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic Sea-Floor Infrastructure for Benthopelagic Monitoring; MarTERA ERA-Net Cofound) and RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades).MDPI2023202320202023info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://hdl.handle.net/10810/59216reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MCIU/TEC2017-87861-R/https://www.mdpi.com/1424-8220/20/3/726info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).oai:addi.ehu.eus:10810/592162026-06-18T09:23:17Z
dc.title.none.fl_str_mv Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
title Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
spellingShingle Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
López Vázquez, Vanesa
cabled observatories
artificial intelligence
deep learning
machine learning
deep-sea fauna
title_short Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
title_full Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
title_fullStr Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
title_full_unstemmed Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
title_sort Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
dc.creator.none.fl_str_mv López Vázquez, Vanesa
López Guede, José Manuel
Marini, Simone
Fanelli, Emanuela
Johnsen, Espen
Aguzzi, Jacopo
author López Vázquez, Vanesa
author_facet López Vázquez, Vanesa
López Guede, José Manuel
Marini, Simone
Fanelli, Emanuela
Johnsen, Espen
Aguzzi, Jacopo
author_role author
author2 López Guede, José Manuel
Marini, Simone
Fanelli, Emanuela
Johnsen, Espen
Aguzzi, Jacopo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv cabled observatories
artificial intelligence
deep learning
machine learning
deep-sea fauna
topic cabled observatories
artificial intelligence
deep learning
machine learning
deep-sea fauna
description Corrección de una afiliación en Sensors 2023, 23, 16. https://doi.org/10.3390/s23010016
publishDate 2020
dc.date.none.fl_str_mv 2020
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/59216
url http://hdl.handle.net/10810/59216
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MCIU/TEC2017-87861-R/
https://www.mdpi.com/1424-8220/20/3/726
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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