Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training

Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training

11 pages, 3 figures, 5 tables, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2023.1151758/full#supplementary-material.-- Data availability statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material

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Bibliographic Details
Main Authors: Catalán, Ignacio Alberto, Álvarez-Ellacuria, Amaya, Lisani, José Luis, Sánchez, Josep, Vizoso, Guillermo, Heinrichs-Maquilón, Antoni Enric, Hinz, Hilmar, Alós, Josep, Signaroli, Marco, Aguzzi, Jacopo, Francescangeli, Marco, Palmer, Miquel
Other Authors: Govern de les Illes Balears
Format: artículo biblioteca
Language:English
Published: Frontiers Media 2023-04
Subjects:Deep learning, Mediterranean, Fish, Pre-treatment, YOLOv5, EfficientNet, Faster RCNN, Conserve and sustainably use the oceans, seas and marine resources for sustainable development,
Online Access:http://hdl.handle.net/10261/311771
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