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, Toni, López-Brosa, Pere, Borrell Thió, Assumpció, Aguilar, Àlex, Borja-Robalino, Ricardo, Cardona Pascual, Luis, Vighi, Morgana
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/182945
Acceso en línea:https://hdl.handle.net/2445/182945
Access Level:acceso abierto
Palabra clave:Teledetecció
Aprenentatge automàtic
Xarxes neuronals convolucionals
Residus
Remote sensing
Machine learning
Convolutional neural networks
Waste products
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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, ToniLópez-Brosa, PereBorrell Thió, AssumpcióAguilar, ÀlexBorja-Robalino, RicardoCardona Pascual, LuisVighi, MorganaTeledeteccióAprenentatge automàticXarxes neuronals convolucionalsResidusRemote sensingMachine learningConvolutional neural networksWaste productsThe 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.Elsevier B.V.2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/182945Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1016/j.envpol.2021.116490Environmental Pollution, 2021, vol. 273, num. 116490, p. 1-11https://doi.org/10.1016/j.envpol.2021.116490cc-by (c) Garcia-Garin et al., 2021http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1829452026-05-27T06:46:51Z
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
Teledetecció
Aprenentatge automàtic
Xarxes neuronals convolucionals
Residus
Remote sensing
Machine learning
Convolutional neural networks
Waste products
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, Toni
López-Brosa, Pere
Borrell Thió, Assumpció
Aguilar, Àlex
Borja-Robalino, Ricardo
Cardona Pascual, Luis
Vighi, Morgana
author Garcia-Garin, Odei
author_facet Garcia-Garin, Odei
Monleón Getino, Toni
López-Brosa, Pere
Borrell Thió, Assumpció
Aguilar, Àlex
Borja-Robalino, Ricardo
Cardona Pascual, Luis
Vighi, Morgana
author_role author
author2 Monleón Getino, Toni
López-Brosa, Pere
Borrell Thió, Assumpció
Aguilar, Àlex
Borja-Robalino, Ricardo
Cardona Pascual, Luis
Vighi, Morgana
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Teledetecció
Aprenentatge automàtic
Xarxes neuronals convolucionals
Residus
Remote sensing
Machine learning
Convolutional neural networks
Waste products
topic Teledetecció
Aprenentatge automàtic
Xarxes neuronals convolucionals
Residus
Remote sensing
Machine learning
Convolutional neural networks
Waste products
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/182945
url https://hdl.handle.net/2445/182945
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.envpol.2021.116490
Environmental Pollution, 2021, vol. 273, num. 116490, p. 1-11
https://doi.org/10.1016/j.envpol.2021.116490
dc.rights.none.fl_str_mv cc-by (c) Garcia-Garin et al., 2021
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Garcia-Garin et al., 2021
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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