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

López-Vázquez, Vanesa; López-Guede, José Manuel; Marini, Simone; Fanelli, Emanuela; Johnsen, Espen; Aguzzi, Jacopo; Agencia Estatal de Investigación (España)

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

Special issue Imaging Sensor Systems for Analyzing Subsea Environment and Life).-- 25 pages, 8 figures, 4 tables, 1 appendix

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Bibliographic Details
Main Authors:	López-Vázquez, Vanesa, López-Guede, José Manuel, Marini, Simone, Fanelli, Emanuela, Johnsen, Espen, Aguzzi, Jacopo
Other Authors:	Agencia Estatal de Investigación (España)
Format:	artículo biblioteca
Published:	Molecular Diversity Preservation International 2020-01
Subjects:	Cabled observatories, Artificial intelligence, Deep learning, Machine learning, Deep-sea fauna,
Online Access:	http://hdl.handle.net/10261/201975 http://dx.doi.org/10.13039/501100011033
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id	dig-icm-es-10261-201975
record_format	koha
spelling	dig-icm-es-10261-2019752023-02-06T11:22:57Z Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories López-Vázquez, Vanesa López-Guede, José Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo Agencia Estatal de Investigación (España) Ministerio de Ciencia, Innovación y Universidades (España) Agencia Estatal de Investigación (España) Cabled observatories Artificial intelligence Deep learning Machine learning Deep-sea fauna Special issue Imaging Sensor Systems for Analyzing Subsea Environment and Life).-- 25 pages, 8 figures, 4 tables, 1 appendix 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) With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) 2020-02-26T08:16:18Z 2020-02-26T08:16:18Z 2020-01 2020-02-26T08:16:18Z artículo http://purl.org/coar/resource_type/c_6501 issn: 1424-3210 e-issn: 1424-8220 Sensors 20(3): 726 (2020) CEX2019-000928-S http://hdl.handle.net/10261/201975 10.3390/s20030726 http://dx.doi.org/10.13039/501100011033 32012976 #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# TEC2017-87861-R/AEI/10.13039/501100011033 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TEC2017-87861-R https://doi.org/10.3390/s20030726 Sí open Molecular Diversity Preservation International
institution	ICM ES
collection	DSpace
country	España
countrycode	ES
component	Bibliográfico
access	En linea
databasecode	dig-icm-es
tag	biblioteca
region	Europa del Sur
libraryname	Biblioteca del ICM España
topic	Cabled observatories Artificial intelligence Deep learning Machine learning Deep-sea fauna Cabled observatories Artificial intelligence Deep learning Machine learning Deep-sea fauna
spellingShingle	Cabled observatories Artificial intelligence Deep learning Machine learning Deep-sea fauna Cabled observatories Artificial intelligence Deep learning Machine learning Deep-sea fauna López-Vázquez, 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
description	Special issue Imaging Sensor Systems for Analyzing Subsea Environment and Life).-- 25 pages, 8 figures, 4 tables, 1 appendix
author2	Agencia Estatal de Investigación (España)
author_facet	Agencia Estatal de Investigación (España) López-Vázquez, Vanesa López-Guede, José Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo
format	artículo
topic_facet	Cabled observatories Artificial intelligence Deep learning Machine learning Deep-sea fauna
author	López-Vázquez, Vanesa López-Guede, José Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo
author_sort	López-Vázquez, 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	Molecular Diversity Preservation International
publishDate	2020-01
url	http://hdl.handle.net/10261/201975 http://dx.doi.org/10.13039/501100011033
work_keys_str_mv	AT lopezvazquezvanesa videoimageenhancementandmachinelearningpipelineforunderwateranimaldetectionandclassificationatcabledobservatories AT lopezguedejosemanuel videoimageenhancementandmachinelearningpipelineforunderwateranimaldetectionandclassificationatcabledobservatories AT marinisimone videoimageenhancementandmachinelearningpipelineforunderwateranimaldetectionandclassificationatcabledobservatories AT fanelliemanuela videoimageenhancementandmachinelearningpipelineforunderwateranimaldetectionandclassificationatcabledobservatories AT johnsenespen videoimageenhancementandmachinelearningpipelineforunderwateranimaldetectionandclassificationatcabledobservatories AT aguzzijacopo videoimageenhancementandmachinelearningpipelineforunderwateranimaldetectionandclassificationatcabledobservatories
_version_	1777667126453075968

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

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