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dc.contributor.advisorBjørndalen, John Markus
dc.contributor.authorMathiassen, Truls
dc.date.accessioned2021-02-04T11:47:17Z
dc.date.available2021-02-04T11:47:17Z
dc.date.issued2020-11-10en
dc.description.abstractWhile monitoring rodents in the Arctic Tundra to evaluate if climate changes affect the ecosystem. The camera-traps of the coat project generates image data in large scale each year. To manually examine the data in regards to label- ing is a tedious and time-consuming job, and a more efficient and automated tool for the task is required. In this thesis we presents the architecture, design and implementation of a object classification model deployed on a small embedded computer, to be used on the gathered image data in order to classify and label the animals at the edge. We conduct transfer-learning on the state-of-the-art pre-trained YOLOv4-tiny model by introducing a labeled COAT image set. We utilize the Convolutional Neural Network of the model to do predictions on a test image set in order to evaluate the model. The result is an application with an embedded model able to predict labels with an accuracy of 96.07% and inference time that classifies it to do so in real-time.en_US
dc.identifier.urihttps://hdl.handle.net/10037/20516
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2020 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDINF-3981
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Computer technology: 551en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Annen informasjonsteknologi: 559en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Other information technology: 559en_US
dc.titleObject detection at the edgeen_US
dc.typeMastergradsoppgavenor
dc.typeMaster thesiseng


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)