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dc.contributor.authorKalhagen, Espen Stausland
dc.contributor.authorOlsen, Ørjan Langøy
dc.date.accessioned2020-10-15T11:23:19Z
dc.date.available2020-10-15T11:23:19Z
dc.date.issued2020
dc.identifier.citationKalhagen, E. S. & Olsen, Ø. L. (2020) Hierarchical Fish Species Detection in Real-Time Video Using YOLO (Master's thesis). University of Agder, Grimstaden_US
dc.identifier.urihttps://hdl.handle.net/11250/2683060
dc.descriptionMaster's thesis in Information- and communication technology (IKT590)en_US
dc.description.abstractInformation gathering of aquatic life is often based on time consuming methods with a foundation in video feeds. It would be beneficial to capture more information in a cost effective manner from video feeds, and video object detection has an opportunity to achieve this. Recent research has shown promising results with the use of YOLO for object detection of fish. As under-water conditions can be difficult and fish species hard to discriminate, we propose the use of a hierarchical structures in both the classification and the dataset to gain valuable information. With the use of hierarchical classification and other techniques we present YOLO Fish. YOLO Fish is a state of the art object detector on nordic fish species, with an mAP of 91.8%. For a more stable video, YOLO Fish can be used with the object tracking algorithm SORT. This results in a complete fish detector for real-time video.en_US
dc.language.isoengen_US
dc.publisherUniversity of Agderen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectIKT590en_US
dc.titleHierarchical Fish Species Detection in Real-Time Video Using YOLOen_US
dc.typeMaster thesisen_US
dc.rights.holder© 2020 Espen Stausland Kalhagen, Ørjan Langøy Olsenen_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.source.pagenumber70en_US


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