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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| Language: | English |
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Frontiers Media
2023-04
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| 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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dig-icm-es-10261-3117712023-06-19T07:30:45Z Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training 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 Govern de les Illes Balears Agencia Estatal de Investigación (España) Deep learning Mediterranean Fish Pre-treatment YOLOv5 EfficientNet Faster RCNN Conserve and sustainably use the oceans, seas and marine resources for sustainable development 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 Further investigation is needed to improve the identification and classification of fish in underwater images using artificial intelligence, specifically deep learning. Questions that need to be explored include the importance of using diverse backgrounds, the effect of (not) labeling small fish on precision, the number of images needed for successful classification, and whether they should be randomly selected. To address these questions, a new labeled dataset was created with over 18,400 recorded Mediterranean fish from 20 species from over 1,600 underwater images with different backgrounds. Two state-of-the-art object detectors/classifiers, YOLOv5m and Faster RCNN, were compared for the detection of the ‘fish’ category in different datasets. YOLOv5m performed better and was thus selected for classifying an increasing number of species in six combinations of labeled datasets varying in background types, balanced or unbalanced number of fishes per background, number of labeled fish, and quality of labeling. Results showed that i) it is cost-efficient to work with a reduced labeled set (a few hundred labeled objects per category) if images are carefully selected, ii) the usefulness of the trained model for classifying unseen datasets improves with the use of different backgrounds in the training dataset, and iii) avoiding training with low-quality labels (e.g., small relative size or incomplete silhouettes) yields better classification metrics. These results and dataset will help select and label images in the most effective way to improve the use of deep learning in studying underwater organisms Project DEEP-ECOMAR. 10.13039/100018685-Comunitat Autonoma de les Illes Balears through the Direcció General de Política Universitària i Recerca with funds from the Tourist Stay Tax law ITS 2017-006 (Grant Number: PRD2018/26). [...] The present research was carried out within the framework of the activities of the Spanish Government through the “María de Maeztu Centre of Excellence” accreditation to IMEDEA (CSIC-UIB) (CEX2021-001198-M) and the “Severo Ochoa Centre Excellence” accreditation to ICM-CSIC (CEX2019-000928-S) and the Research Unit Tecnoterra (ICM-CSIC/UPC) Peer reviewed 2023-06-19T07:28:50Z 2023-06-19T07:28:50Z 2023-04 artículo Frontiers in Marine Science 10: 1151758 (2023) CEX2019-000928-S CEX2021-001198-M http://hdl.handle.net/10261/311771 10.3389/fmars.2023.1151758 2296-7745 en Publisher's version https://doi.org/10.3389/fmars.2023.1151758 Sí open Frontiers Media |
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Deep learning Mediterranean Fish Pre-treatment YOLOv5 EfficientNet Faster RCNN Conserve and sustainably use the oceans, seas and marine resources for sustainable development Deep learning Mediterranean Fish Pre-treatment YOLOv5 EfficientNet Faster RCNN Conserve and sustainably use the oceans, seas and marine resources for sustainable development |
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Deep learning Mediterranean Fish Pre-treatment YOLOv5 EfficientNet Faster RCNN Conserve and sustainably use the oceans, seas and marine resources for sustainable development Deep learning Mediterranean Fish Pre-treatment YOLOv5 EfficientNet Faster RCNN Conserve and sustainably use the oceans, seas and marine resources for sustainable development 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 Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training |
| description |
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 |
| author2 |
Govern de les Illes Balears |
| author_facet |
Govern de les Illes Balears 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 |
| format |
artículo |
| topic_facet |
Deep learning Mediterranean Fish Pre-treatment YOLOv5 EfficientNet Faster RCNN Conserve and sustainably use the oceans, seas and marine resources for sustainable development |
| author |
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 |
| author_sort |
Catalán, Ignacio Alberto |
| title |
Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training |
| title_short |
Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training |
| title_full |
Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training |
| title_fullStr |
Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training |
| title_full_unstemmed |
Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training |
| title_sort |
automatic detection and classification of coastal mediterranean fish from underwater images: good practices for robust training |
| publisher |
Frontiers Media |
| publishDate |
2023-04 |
| url |
http://hdl.handle.net/10261/311771 |
| work_keys_str_mv |
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