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dc.contributor.authorLopez-vazquez, Vanesa
dc.contributor.authorLopez-guede, Jose Manuel
dc.contributor.authorMarini, Simone
dc.contributor.authorFanelli, Emanuela
dc.contributor.authorJohnsen, Espen
dc.contributor.authorAguzzi, J.
dc.date.accessioned2021-02-02T09:46:10Z
dc.date.available2021-02-02T09:46:10Z
dc.date.created2021-01-18T12:26:05Z
dc.date.issued2020
dc.identifier.citationSensors. 2020, 20 (3), 1-25.en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2725733
dc.description.abstractAn 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%.en_US
dc.language.isoengen_US
dc.titleVideo Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatoriesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1-25en_US
dc.source.volume20en_US
dc.source.journalSensorsen_US
dc.source.issue3en_US
dc.identifier.doi10.3390/s20030726
dc.identifier.cristin1873152
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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