Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring

Deep learning has emerged as a powerful tool for automated object detection, offering unprecedented speed and accuracy in analyzing complex visual data. In the context of marine ecosystem monitoring, convolutional neural networks (CNNs), particularly YOLO-based architectures, have demonstrated remar...

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Autores: Prat Bayarri, Oriol, Baños Castelló, Pol|||0000-0003-0780-3255, Martínez Padró, Enoc|||0000-0003-1233-7105, Francescangeli, Marco, Toma, Daniel|||0000-0003-0472-1190, Carandell Widmer, Matias|||0000-0003-0559-4453, Prat Farran, Joana d'Arc|||0000-0001-7628-487X, Río Fernández, Joaquín del|||0000-0002-6191-2201
Formato: capítulo de livro
Fecha de publicación:2025
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/439139
Acesso em linha:https://hdl.handle.net/2117/439139
https://dx.doi.org/10.5772/intechopen.1011280
Access Level:acceso abierto
Palavra-chave:Deep learning
Fish detection
YOLO
Underwater imagery
AI-assisted labeling
Marine ecosystem monitoring
Convolutional neural networks
Object detection
Machine learning
Ecological data analysis
Marine species classification
Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura
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repository_id_str
spelling Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoringPrat Bayarri, OriolBaños Castelló, Pol|||0000-0003-0780-3255Martínez Padró, Enoc|||0000-0003-1233-7105Francescangeli, MarcoToma, Daniel|||0000-0003-0472-1190Carandell Widmer, Matias|||0000-0003-0559-4453Prat Farran, Joana d'Arc|||0000-0001-7628-487XRío Fernández, Joaquín del|||0000-0002-6191-2201Deep learningFish detectionYOLOUnderwater imageryAI-assisted labelingMarine ecosystem monitoringConvolutional neural networksObject detectionMachine learningEcological data analysisMarine species classificationArtificial intelligenceÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialÀrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesuraDeep learning has emerged as a powerful tool for automated object detection, offering unprecedented speed and accuracy in analyzing complex visual data. In the context of marine ecosystem monitoring, convolutional neural networks (CNNs), particularly YOLO-based architectures, have demonstrated remarkable efficiency in detecting and classifying fish species in underwater imagery. Traditional fish identification methods rely on manual annotation, which is both time-consuming and prone to inconsistencies. By implementing a semi-automated labeling approach, where human experts refine AI-generated predictions, the annotation process can be streamlined while ensuring taxonomic precision. A key aspect of this research is the creation of a comprehensive training guide that optimizes the model’s performance by detailing best practices in dataset preparation, annotation techniques, hyperparameter tuning, and augmentation strategies. Using a dataset derived from the OBSEA marine observatory, results indicate that the YOLO extra-large model, trained with a small learning rate and high-resolution images, achieves optimal performance in fish identification. The findings underscore the potential of AI-assisted methodologies in ecological research, offering a scalable and efficient alternative to manual annotation for sustainable marine biodiversity monitoring.This work has been supported by various funding sources and research initiatives. We acknowledge the financial support from grants 2023 INV-2 00044 (position codes 200044TC31 and 200044TC6). Additionally, this research has been funded by the European Commission’s HORIZON-INFRA-2021-SERV-01 program under the iMagine project (grant agreement 101058625). We also recognize the use of the EGI infrastructure with dedicated support from EGI-IFCA-STACK, which contributed to the computational resources required for this study. Furthermore, the researchers wish to acknowledge the support of the Associated Unit Tecnoterra, composed of members from UPC and ICM-CSIC, for their valuable collaboration in this work.Peer ReviewedIntechOpen20252025-07-2120252025-07-22book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://hdl.handle.net/2117/439139https://dx.doi.org/10.5772/intechopen.1011280reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/501100000780 HE 101058625 Imaging data and services for aquatic scienceEuropean Commission http://doi.org/10.13039/501100000780 HE 101094924 operAtional seNsing lifE technologies for maRIne ecosystemSEuropean Commission http://doi.org/10.13039/501100000780 HE 101112883 Digital Twin-sustained 4D ecological monitoring of restoration in fishery depleted areasopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4391392026-05-27T15:37:01Z
dc.title.none.fl_str_mv Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
title Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
spellingShingle Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
Prat Bayarri, Oriol
Deep learning
Fish detection
YOLO
Underwater imagery
AI-assisted labeling
Marine ecosystem monitoring
Convolutional neural networks
Object detection
Machine learning
Ecological data analysis
Marine species classification
Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura
title_short Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
title_full Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
title_fullStr Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
title_full_unstemmed Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
title_sort Deep learning for automated fish detection in underwater images: a tool for sustainable marine ecosystem monitoring
dc.creator.none.fl_str_mv Prat Bayarri, Oriol
Baños Castelló, Pol|||0000-0003-0780-3255
Martínez Padró, Enoc|||0000-0003-1233-7105
Francescangeli, Marco
Toma, Daniel|||0000-0003-0472-1190
Carandell Widmer, Matias|||0000-0003-0559-4453
Prat Farran, Joana d'Arc|||0000-0001-7628-487X
Río Fernández, Joaquín del|||0000-0002-6191-2201
author Prat Bayarri, Oriol
author_facet Prat Bayarri, Oriol
Baños Castelló, Pol|||0000-0003-0780-3255
Martínez Padró, Enoc|||0000-0003-1233-7105
Francescangeli, Marco
Toma, Daniel|||0000-0003-0472-1190
Carandell Widmer, Matias|||0000-0003-0559-4453
Prat Farran, Joana d'Arc|||0000-0001-7628-487X
Río Fernández, Joaquín del|||0000-0002-6191-2201
author_role author
author2 Baños Castelló, Pol|||0000-0003-0780-3255
Martínez Padró, Enoc|||0000-0003-1233-7105
Francescangeli, Marco
Toma, Daniel|||0000-0003-0472-1190
Carandell Widmer, Matias|||0000-0003-0559-4453
Prat Farran, Joana d'Arc|||0000-0001-7628-487X
Río Fernández, Joaquín del|||0000-0002-6191-2201
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Deep learning
Fish detection
YOLO
Underwater imagery
AI-assisted labeling
Marine ecosystem monitoring
Convolutional neural networks
Object detection
Machine learning
Ecological data analysis
Marine species classification
Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura
topic Deep learning
Fish detection
YOLO
Underwater imagery
AI-assisted labeling
Marine ecosystem monitoring
Convolutional neural networks
Object detection
Machine learning
Ecological data analysis
Marine species classification
Artificial intelligence
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura
description Deep learning has emerged as a powerful tool for automated object detection, offering unprecedented speed and accuracy in analyzing complex visual data. In the context of marine ecosystem monitoring, convolutional neural networks (CNNs), particularly YOLO-based architectures, have demonstrated remarkable efficiency in detecting and classifying fish species in underwater imagery. Traditional fish identification methods rely on manual annotation, which is both time-consuming and prone to inconsistencies. By implementing a semi-automated labeling approach, where human experts refine AI-generated predictions, the annotation process can be streamlined while ensuring taxonomic precision. A key aspect of this research is the creation of a comprehensive training guide that optimizes the model’s performance by detailing best practices in dataset preparation, annotation techniques, hyperparameter tuning, and augmentation strategies. Using a dataset derived from the OBSEA marine observatory, results indicate that the YOLO extra-large model, trained with a small learning rate and high-resolution images, achieves optimal performance in fish identification. The findings underscore the potential of AI-assisted methodologies in ecological research, offering a scalable and efficient alternative to manual annotation for sustainable marine biodiversity monitoring.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-07-21
2025
2025-07-22
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/439139
https://dx.doi.org/10.5772/intechopen.1011280
url https://hdl.handle.net/2117/439139
https://dx.doi.org/10.5772/intechopen.1011280
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/501100000780 HE 101058625 Imaging data and services for aquatic science
European Commission http://doi.org/10.13039/501100000780 HE 101094924 operAtional seNsing lifE technologies for maRIne ecosystemS
European Commission http://doi.org/10.13039/501100000780 HE 101112883 Digital Twin-sustained 4D ecological monitoring of restoration in fishery depleted areas
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IntechOpen
publisher.none.fl_str_mv IntechOpen
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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