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Image dataset for benchmarking automated fish detection and classification algorithms

Articolo
Data di Pubblicazione:
2023
Abstract:
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?inspacekm from Vilanova i la Geltr'u at 20?inspacem 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.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
Marine observatory; underwater images; OBSEA; scientific dataset
Elenco autori:
Marini, Simone
Autori di Ateneo:
MARINI SIMONE
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/418614
Pubblicato in:
SCIENTIFIC DATA
Journal
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URL

https://doi.org/10.1038/s41597-022-01906-1
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