Deep learning based deep-sea automatic image enhancement and animal species classification

López-Vázquez, Vanesa; López-Guede, José Manuel; Chatzievangelou, Damianos; Aguzzi, Jacopo; Centro para el Desarrollo Tecnológico Industrial (España)

Deep learning based deep-sea automatic image enhancement and animal species classification

26 pages, 13 figures, 9 tables.-- Availability of data and materials: All raw footage used in this study were archived and are available in the Oceans 2.0 database (data.oceannetworks.ca/DataSearch).

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Bibliographic Details
Main Authors:	López-Vázquez, Vanesa, López-Guede, José Manuel, Chatzievangelou, Damianos, Aguzzi, Jacopo
Other Authors:	Centro para el Desarrollo Tecnológico Industrial (España)
Format:	artículo biblioteca
Language:	English
Published:	Springer 2023-03
Subjects:	Conserve and sustainably use the oceans, seas and marine resources for sustainable development,
Online Access:	http://hdl.handle.net/10261/305428
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id	dig-icm-es-10261-305428
record_format	koha
spelling	dig-icm-es-10261-3054282023-04-03T10:58:59Z Deep learning based deep-sea automatic image enhancement and animal species classification López-Vázquez, Vanesa López-Guede, José Manuel Chatzievangelou, Damianos Aguzzi, Jacopo Centro para el Desarrollo Tecnológico Industrial (España) Agencia Estatal de Investigación (España) Conserve and sustainably use the oceans, seas and marine resources for sustainable development 26 pages, 13 figures, 9 tables.-- Availability of data and materials: All raw footage used in this study were archived and are available in the Oceans 2.0 database (data.oceannetworks.ca/DataSearch). The automatic classification of marine species based on images is a challenging task for which multiple solutions have been increasingly provided in the past two decades. Oceans are complex ecosystems, difficult to access, and often the images obtained are of low quality. In such cases, animal classification becomes tedious. Therefore, it is often necessary to apply enhancement or pre-processing techniques to the images, before applying classification algorithms. In this work, we propose an image enhancement and classification pipeline that allows automated processing of images from benthic moving platforms. Deep-sea (870 m depth) fauna was targeted in footage taken by the crawler “Wally” (an Internet Operated Vehicle), within the Ocean Network Canada (ONC) area of Barkley Canyon (Vancouver, BC; Canada). The image enhancement process consists mainly of a convolutional residual network, capable of generating enhanced images from a set of raw images. The images generated by the trained convolutional residual network obtained high values in metrics for underwater imagery assessment such as UIQM (~ 2.585) and UCIQE (2.406). The highest SSIM and PSNR values were also obtained when compared to the original dataset. The entire process has shown good classification results on an independent test data set, with an accuracy value of 66.44% and an Area Under the ROC Curve (AUROC) value of 82.91%, which were subsequently improved to 79.44% and 88.64% for accuracy and AUROC respectively. These results obtained with the enhanced images are quite promising and superior to those obtained with the non-enhanced datasets, paving the strategy for the on-board real-time processing of crawler imaging, and outperforming those published in previous papers This work was supported by the Centro para el Desarrollo Tecnológico Industrial (CDTI) (Grant No. EXP 00108707 / SERA-20181020 With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) Peer reviewed 2023-04-03T10:58:23Z 2023-04-03T10:58:23Z 2023-03 artículo Journal of Big Data 10: 37 (2023) CEX2019-000928-S http://hdl.handle.net/10261/305428 10.1186/s40537-023-00711-w 2196-1115 en Publisher's version https://doi.org/10.1186/s40537-023-00711-w Sí open Springer
institution	ICM ES
collection	DSpace
country	España
countrycode	ES
component	Bibliográfico
access	En linea
databasecode	dig-icm-es
tag	biblioteca
region	Europa del Sur
libraryname	Biblioteca del ICM España
language	English
topic	Conserve and sustainably use the oceans, seas and marine resources for sustainable development Conserve and sustainably use the oceans, seas and marine resources for sustainable development
spellingShingle	Conserve and sustainably use the oceans, seas and marine resources for sustainable development Conserve and sustainably use the oceans, seas and marine resources for sustainable development López-Vázquez, Vanesa López-Guede, José Manuel Chatzievangelou, Damianos Aguzzi, Jacopo Deep learning based deep-sea automatic image enhancement and animal species classification
description	26 pages, 13 figures, 9 tables.-- Availability of data and materials: All raw footage used in this study were archived and are available in the Oceans 2.0 database (data.oceannetworks.ca/DataSearch).
author2	Centro para el Desarrollo Tecnológico Industrial (España)
author_facet	Centro para el Desarrollo Tecnológico Industrial (España) López-Vázquez, Vanesa López-Guede, José Manuel Chatzievangelou, Damianos Aguzzi, Jacopo
format	artículo
topic_facet	Conserve and sustainably use the oceans, seas and marine resources for sustainable development
author	López-Vázquez, Vanesa López-Guede, José Manuel Chatzievangelou, Damianos Aguzzi, Jacopo
author_sort	López-Vázquez, Vanesa
title	Deep learning based deep-sea automatic image enhancement and animal species classification
title_short	Deep learning based deep-sea automatic image enhancement and animal species classification
title_full	Deep learning based deep-sea automatic image enhancement and animal species classification
title_fullStr	Deep learning based deep-sea automatic image enhancement and animal species classification
title_full_unstemmed	Deep learning based deep-sea automatic image enhancement and animal species classification
title_sort	deep learning based deep-sea automatic image enhancement and animal species classification
publisher	Springer
publishDate	2023-03
url	http://hdl.handle.net/10261/305428
work_keys_str_mv	AT lopezvazquezvanesa deeplearningbaseddeepseaautomaticimageenhancementandanimalspeciesclassification AT lopezguedejosemanuel deeplearningbaseddeepseaautomaticimageenhancementandanimalspeciesclassification AT chatzievangeloudamianos deeplearningbaseddeepseaautomaticimageenhancementandanimalspeciesclassification AT aguzzijacopo deeplearningbaseddeepseaautomaticimageenhancementandanimalspeciesclassification
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Deep learning based deep-sea automatic image enhancement and animal species classification

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