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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-9810604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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