Traffic Analysis Using Machine Learning Approach.

Thumbnail Image
Files
eeict-general-265-268.pdf(1.74 MB)
Date
2022
Authors
Zelený, O.
Frýza, T.
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract
This 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.
Description
Keywords
Deep learning, Computer vision, Traffic analysis, Convolutional Neural Networks, You Only Look Once, COCO dataset
Citation
Proceedings I of the 28st Conference STUDENT EEICT 2022: General papers. s. 265-268. ISBN 978-80-214-6029-4
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence
© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
DOI
URI
http://hdl.handle.net/11012/209342
Collections
Student EEICT 2022
Citace PRO