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Automatic Annotation of Subsea Pipelines Using Deep Learning
Open AccessArticle

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

1
DS Labs, R+D+I unit of Deusto Sistemas S.A., 01015 Vitoria-Gasteiz, Spain
2
Department of System Engineering and Automation Control, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain
3
Institute of Marine Sciences, National Research Council of Italy (CNR), 19032 La Spezia, Italy
4
Stazione Zoologica Anton Dohrn (SZN), 80122 Naples, Italy
5
Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche, 60131 Ancona, Italy
6
Institute of Marine Research, PO Box 1870, 5817 Bergen, Norway
7
Instituto de Ciencias del Mar (ICM) of the Consejo Superior de Investigaciones Científicas (CSIC), 08003 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(3), 726; https://doi.org/10.3390/s20030726
Received: 31 December 2019 / Revised: 24 January 2020 / Accepted: 24 January 2020 / Published: 28 January 2020
(This article belongs to the Special Issue Imaging Sensor Systems for Analyzing Subsea Environment and Life)
An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%. View Full-Text
Keywords: cabled observatories; artificial intelligence; deep learning; machine learning; deep-sea fauna cabled observatories; artificial intelligence; deep learning; machine learning; deep-sea fauna
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Lopez-Vazquez, V.; Lopez-Guede, J.M.; Marini, S.; Fanelli, E.; Johnsen, E.; Aguzzi, J. Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories. Sensors 2020, 20, 726.

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