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Image dataset for benchmarking automated fish detection and classification algorithms

Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, l...

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Autores principales: Francescangeli, Marco, Marini, Simone, Martínez, Enoc, Del Río, Joaquín, Toma, Daniel M., Nogueras, Marc, Aguzzi, Jacopo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810604/
https://www.ncbi.nlm.nih.gov/pubmed/36596792
http://dx.doi.org/10.1038/s41597-022-01906-1
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author Francescangeli, Marco
Marini, Simone
Martínez, Enoc
Del Río, Joaquín
Toma, Daniel M.
Nogueras, Marc
Aguzzi, Jacopo
author_facet Francescangeli, Marco
Marini, Simone
Martínez, Enoc
Del Río, Joaquín
Toma, Daniel M.
Nogueras, Marc
Aguzzi, Jacopo
author_sort Francescangeli, Marco
collection PubMed
description Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013–2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets.
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spelling pubmed-98106042023-01-05 Image dataset for benchmarking automated fish detection and classification algorithms Francescangeli, Marco Marini, Simone Martínez, Enoc Del Río, Joaquín Toma, Daniel M. Nogueras, Marc Aguzzi, Jacopo Sci Data Data Descriptor Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013–2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets. Nature Publishing Group UK 2023-01-03 /pmc/articles/PMC9810604/ /pubmed/36596792 http://dx.doi.org/10.1038/s41597-022-01906-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Francescangeli, Marco
Marini, Simone
Martínez, Enoc
Del Río, Joaquín
Toma, Daniel M.
Nogueras, Marc
Aguzzi, Jacopo
Image dataset for benchmarking automated fish detection and classification algorithms
title Image dataset for benchmarking automated fish detection and classification algorithms
title_full Image dataset for benchmarking automated fish detection and classification algorithms
title_fullStr Image dataset for benchmarking automated fish detection and classification algorithms
title_full_unstemmed Image dataset for benchmarking automated fish detection and classification algorithms
title_short Image dataset for benchmarking automated fish detection and classification algorithms
title_sort image dataset for benchmarking automated fish detection and classification algorithms
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810604/
https://www.ncbi.nlm.nih.gov/pubmed/36596792
http://dx.doi.org/10.1038/s41597-022-01906-1
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