Multi-Class Weather Classification From Single Images With Convolutional Neural Networks On Embedded Hardware

but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.contributor.authorBravenec, Tomáš
dc.date.accessioned2021-07-21T07:06:55Z
dc.date.available2021-07-21T07:06:55Z
dc.date.issued2021cs
dc.description.abstractThe paper is focused on creating a lightweight machine learning solution for classificationof weather conditions from input images, that can process the input data in real time on embeddeddevices. The approach to the classification uses deep convolutional neural networks architecture withfocus on lightweight design and fast inference, while providing high accuracy results. The focus oncreating lightweight convolutional neural network architecture capable of classification of weatherconditions also enables usage of the network in real time applications at the edge.en
dc.formattextcs
dc.format.extent586-590cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 27st Conference STUDENT EEICT 2021: General papers. s. 586-590. ISBN 978-80-214-5942-7cs
dc.identifier.isbn978-80-214-5942-7
dc.identifier.urihttp://hdl.handle.net/11012/200700
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 27st Conference STUDENT EEICT 2021: General papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectdeep learningen
dc.subjectneural networksen
dc.subjectcomputer visionen
dc.subjectweather classificationen
dc.subjectmachine learning,parallel computingen
dc.subjectinference on edgeen
dc.subjectreduced precision computingen
dc.titleMulti-Class Weather Classification From Single Images With Convolutional Neural Networks On Embedded Hardwareen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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