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A State of the Art Technology in Large Scale Underwater Monitoring

Academic Article
Publication Date:
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.
Iris type:
01.01 Articolo in rivista
Keywords:
underwater monitoring; automatic recognition; human-in-the-loop; semantic segmentation; deep learning
List of contributors:
Pavoni, Gaia; Cignoni, Paolo; Corsini, Massimiliano
Authors of the University:
CIGNONI PAOLO
CORSINI MASSIMILIANO
PAVONI GAIA
Handle:
https://iris.cnr.it/handle/20.500.14243/406896
Full Text:
https://iris.cnr.it//retrieve/handle/20.500.14243/406896/89199/prod_423458-doc_150813.pdf
Published 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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