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 An 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 w...
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ftunivpaisvasco:oai:addi.ehu.eus:10810/59216 2023-05-15T15:39:09+02:00 Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories Lopez-Vazquez, Vanesa López Guede, José Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo 2023-01-06T13:52:55Z application/pdf http://hdl.handle.net/10810/59216 https://doi.org/10.3390/s20030726 eng eng MDPI info:eu-repo/grantAgreement/MCIU/TEC2017-87861-R https://www.mdpi.com/1424-8220/20/3/726 Sensors 20(3): (2020) // Article ID 726 1424-8220 http://hdl.handle.net/10810/59216 doi:10.3390/s20030726 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/es/ © 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/). CC-BY cabled observatories artificial intelligence deep learning machine learning deep-sea fauna info:eu-repo/semantics/article 2023 ftunivpaisvasco https://doi.org/10.3390/s20030726 2023-01-11T00:20:59Z Corrección de una afiliación en Sensors 2023, 23, 16. https://doi.org/10.3390/s23010016 An 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). Article in Journal/Newspaper Barents Sea Lofoten ADDI: Repositorio Institucional de la Universidad del País Vasco (UPV) Barents Sea Lofoten Norway Sensors 20 3 726 |
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Open Polar |
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ADDI: Repositorio Institucional de la Universidad del País Vasco (UPV) |
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ftunivpaisvasco |
language |
English |
topic |
cabled observatories artificial intelligence deep learning machine learning deep-sea fauna |
spellingShingle |
cabled observatories artificial intelligence deep learning machine learning deep-sea fauna Lopez-Vazquez, Vanesa López Guede, José Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories |
topic_facet |
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 An 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). |
format |
Article in Journal/Newspaper |
author |
Lopez-Vazquez, Vanesa López Guede, José Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo |
author_facet |
Lopez-Vazquez, Vanesa López Guede, José Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo |
author_sort |
Lopez-Vazquez, Vanesa |
title |
Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories |
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 |
publisher |
MDPI |
publishDate |
2023 |
url |
http://hdl.handle.net/10810/59216 https://doi.org/10.3390/s20030726 |
geographic |
Barents Sea Lofoten Norway |
geographic_facet |
Barents Sea Lofoten Norway |
genre |
Barents Sea Lofoten |
genre_facet |
Barents Sea Lofoten |
op_relation |
info:eu-repo/grantAgreement/MCIU/TEC2017-87861-R https://www.mdpi.com/1424-8220/20/3/726 Sensors 20(3): (2020) // Article ID 726 1424-8220 http://hdl.handle.net/10810/59216 doi:10.3390/s20030726 |
op_rights |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/es/ © 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/). |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/s20030726 |
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Sensors |
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20 |
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3 |
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726 |
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1766370610056790016 |