Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
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 info...
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ftmdpi:oai:mdpi.com:/1424-8220/20/3/726/ 2023-08-20T04:05:31+02:00 Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories Vanesa Lopez-Vazquez Jose Lopez-Guede Simone Marini Emanuela Fanelli Espen Johnsen Jacopo Aguzzi 2020-01-28 application/pdf https://doi.org/10.3390/s20030726 EN eng Multidisciplinary Digital Publishing Institute Intelligent Sensors https://dx.doi.org/10.3390/s20030726 https://creativecommons.org/licenses/by/4.0/ Sensors; Volume 20; Issue 3; Pages: 726 cabled observatories artificial intelligence deep learning machine learning deep-sea fauna Text 2020 ftmdpi https://doi.org/10.3390/s20030726 2023-07-31T23:02:59Z 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%. Text Barents Sea Lofoten MDPI Open Access Publishing Barents Sea Lofoten Norway Sensors 20 3 726 |
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English |
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cabled observatories artificial intelligence deep learning machine learning deep-sea fauna |
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cabled observatories artificial intelligence deep learning machine learning deep-sea fauna Vanesa Lopez-Vazquez Jose Lopez-Guede Simone Marini Emanuela Fanelli Espen Johnsen Jacopo Aguzzi Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories |
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cabled observatories artificial intelligence deep learning machine learning deep-sea fauna |
description |
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%. |
format |
Text |
author |
Vanesa Lopez-Vazquez Jose Lopez-Guede Simone Marini Emanuela Fanelli Espen Johnsen Jacopo Aguzzi |
author_facet |
Vanesa Lopez-Vazquez Jose Lopez-Guede Simone Marini Emanuela Fanelli Espen Johnsen Jacopo Aguzzi |
author_sort |
Vanesa Lopez-Vazquez |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
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_source |
Sensors; Volume 20; Issue 3; Pages: 726 |
op_relation |
Intelligent Sensors https://dx.doi.org/10.3390/s20030726 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
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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1774716054192259072 |