Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

The threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new...

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Autores: Garcia Garin, Odei, Monleón Getino, Antonio|||0000-0001-8214-3205, López Brosa, Pere, Borrell i Thio, Assumpció, Aguilar Sánchez, Alex, Borja Robalino, Ricardo Stalin|||0000-0002-3899-1140, Cardona, Luís, Vighi, Morgana
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/361223
Acceso en línea:https://hdl.handle.net/2117/361223
https://dx.doi.org/10.1016/j.envpol.2021.116490
Access Level:acceso abierto
Palabra clave:Machine learning
Remote sensing
Unmanned aerial vehicles
Convolutional neural network
Marine litter
Mar -- Residus
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
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spelling Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in RGarcia Garin, OdeiMonleón Getino, Antonio|||0000-0001-8214-3205López Brosa, PereBorrell i Thio, AssumpcióAguilar Sánchez, AlexBorja Robalino, Ricardo Stalin|||0000-0002-3899-1140Cardona, LuísVighi, MorganaMachine learningRemote sensingRemote sensingMachine learningUnmanned aerial vehiclesConvolutional neural networkMarine litterMar -- ResidusControl automàticÀrees temàtiques de la UPC::Informàtica::Automàtica i controlThe threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new technologies and the automation of analytical processes. In the current study, we developed algorithms capable of detecting and quantifying FMML in aerial images, and a web-oriented application that allows users to identify FMML within images of the sea surface. The proposed algorithm is based on a deep learning approach that uses convolutional neural networks (CNNs) capable of learning from unstructured or unlabelled data. The CNN-based deep learning model was trained and tested using 3723 aerial images (50% containing FMML, 50% without FMML) taken by drones and aircraft over the waters of the NW Mediterranean Sea. The accuracies of image classification (performed using all the images for training and testing the model) and cross-validation (performed using 90% of images for training and 10% for testing) were 0.85 and 0.81, respectively. The Shiny package of R was then used to develop a user-friendly application to identify and quantify FMML within the aerial images. The implementation of this, and similar algorithms, allows streamlining substantially the detection and quantification of FMML, providing support to the monitoring and assessment of this environmental threat. However, the automated monitoring of FMML in the open sea still represents a technological challenge, and further research is needed to improve the accuracy of current algorithms.Peer Reviewed20212021-03-1520222022-02-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/361223https://dx.doi.org/10.1016/j.envpol.2021.116490reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3612232026-05-27T15:37:01Z
dc.title.none.fl_str_mv Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
title Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
spellingShingle Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
Garcia Garin, Odei
Machine learning
Remote sensing
Remote sensing
Machine learning
Unmanned aerial vehicles
Convolutional neural network
Marine litter
Mar -- Residus
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
title_short Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
title_full Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
title_fullStr Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
title_full_unstemmed Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
title_sort Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
dc.creator.none.fl_str_mv Garcia Garin, Odei
Monleón Getino, Antonio|||0000-0001-8214-3205
López Brosa, Pere
Borrell i Thio, Assumpció
Aguilar Sánchez, Alex
Borja Robalino, Ricardo Stalin|||0000-0002-3899-1140
Cardona, Luís
Vighi, Morgana
author Garcia Garin, Odei
author_facet Garcia Garin, Odei
Monleón Getino, Antonio|||0000-0001-8214-3205
López Brosa, Pere
Borrell i Thio, Assumpció
Aguilar Sánchez, Alex
Borja Robalino, Ricardo Stalin|||0000-0002-3899-1140
Cardona, Luís
Vighi, Morgana
author_role author
author2 Monleón Getino, Antonio|||0000-0001-8214-3205
López Brosa, Pere
Borrell i Thio, Assumpció
Aguilar Sánchez, Alex
Borja Robalino, Ricardo Stalin|||0000-0002-3899-1140
Cardona, Luís
Vighi, Morgana
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Machine learning
Remote sensing
Remote sensing
Machine learning
Unmanned aerial vehicles
Convolutional neural network
Marine litter
Mar -- Residus
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
topic Machine learning
Remote sensing
Remote sensing
Machine learning
Unmanned aerial vehicles
Convolutional neural network
Marine litter
Mar -- Residus
Control automàtic
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
description The threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new technologies and the automation of analytical processes. In the current study, we developed algorithms capable of detecting and quantifying FMML in aerial images, and a web-oriented application that allows users to identify FMML within images of the sea surface. The proposed algorithm is based on a deep learning approach that uses convolutional neural networks (CNNs) capable of learning from unstructured or unlabelled data. The CNN-based deep learning model was trained and tested using 3723 aerial images (50% containing FMML, 50% without FMML) taken by drones and aircraft over the waters of the NW Mediterranean Sea. The accuracies of image classification (performed using all the images for training and testing the model) and cross-validation (performed using 90% of images for training and 10% for testing) were 0.85 and 0.81, respectively. The Shiny package of R was then used to develop a user-friendly application to identify and quantify FMML within the aerial images. The implementation of this, and similar algorithms, allows streamlining substantially the detection and quantification of FMML, providing support to the monitoring and assessment of this environmental threat. However, the automated monitoring of FMML in the open sea still represents a technological challenge, and further research is needed to improve the accuracy of current algorithms.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-03-15
2022
2022-02-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/361223
https://dx.doi.org/10.1016/j.envpol.2021.116490
url https://hdl.handle.net/2117/361223
https://dx.doi.org/10.1016/j.envpol.2021.116490
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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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