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A State of the Art Technology in Large Scale Underwater Monitoring

Articolo
Data di Pubblicazione:
2020
Abstract:
In recent decades, benthic populations have been subjected to recurrent episodes of mass mortality. These events have been blamed in part on declining water quality and elevated water temperatures (see Figure 1) correlated to global climate change. Ecosystems are enhanced by the presence of species with three-dimensional growth. The study of the growth, resilience, and recovery capability of those species provides valuable information on the conservation status of entire habitats. We discuss here a state-of-the art solution to speed up the monitoring of benthic population through the automatic or assisted analysis of underwater visual data.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
underwater monitoring; automatic recognition; human-in-the-loop; semantic segmentation; deep learning
Elenco autori:
Pavoni, Gaia; Cignoni, Paolo; Corsini, Massimiliano
Autori di Ateneo:
CIGNONI PAOLO
CORSINI MASSIMILIANO
PAVONI GAIA
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/406896
Link al Full Text:
https://iris.cnr.it//retrieve/handle/20.500.14243/406896/89199/prod_423458-doc_150813.pdf
Pubblicato in:
ERCIM NEWS
Journal
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URL

https://ercim-news.ercim.eu/en121/special/a-state-of-the-art-technology-in-large-scale-underwater-monitoring
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