Traffic Analysis Using Machine Learning Approach.

Simple item page
but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorZelený, O.
dc.contributor.authorFrýza, T.
dc.date.accessioned2023-04-25T10:17:08Z
dc.date.available2023-04-25T10:17:08Z
dc.date.issued2022cs
dc.description.abstractThis paper provides insight to the YOLOv5 deep learning architecture and its use for vehicle detection and classification in order to improve traffic management in larger cities and busy roads. The paper presents simple system with one fixed camera and Jetson Nano, a computer for embedded and AI application, to detect and classify vehicles.en
dc.formattextcs
dc.format.extent265-268cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 28st Conference STUDENT EEICT 2022: General papers. s. 265-268. ISBN 978-80-214-6029-4cs
dc.identifier.isbn978-80-214-6029-4
dc.identifier.urihttp://hdl.handle.net/11012/209342
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 28st Conference STUDENT EEICT 2022: 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.subjectComputer visionen
dc.subjectTraffic analysisen
dc.subjectConvolutional Neural Networksen
dc.subjectYou Only Look Onceen
dc.subjectCOCO dataseten
dc.titleTraffic Analysis Using Machine Learning Approach.en
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
eeict-general-265-268.pdf
Size:
1.74 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Student EEICT 2022