Result Details

Traffic Analysis Using Machine Learning Approach

ZELENÝ, O.; FRÝZA, T. Traffic Analysis Using Machine Learning Approach. In PROCEEDINGS I OF THE 28TH STUDENT EEICT 2022 General papers. 1. Brno: Brno University of Technology, Faculty of ERlectronic Engineering and Communication, 2022. p. 265-268. ISBN: 978-80-214-6029-4.
Type
conference paper
Language
English
Authors
Zelený Ondřej, Ing., FEEC (FEEC)
Frýza Tomáš, doc. Ing., Ph.D., UREL (FEEC)
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.

Keywords

Deep learning, Computer vision, Traffic analysis, Convolutional Neural
Networks, You Only Look Once, COCO dataset

URL
Published
2022
Pages
265–268
Proceedings
PROCEEDINGS I OF THE 28TH STUDENT EEICT 2022 General papers
Series
1
Conference
STUDENT EEICT 2022
ISBN
978-80-214-6029-4
Publisher
Brno University of Technology, Faculty of ERlectronic Engineering and Communication
Place
Brno
EID Scopus
2-s2.0-85162016983
BibTeX 
@inproceedings{BUT186978,
  author="Ondřej {Zelený} and Tomáš {Frýza}",
  title="Traffic Analysis Using Machine Learning Approach",
  booktitle="PROCEEDINGS I OF THE 28TH STUDENT EEICT 2022 General papers",
  year="2022",
  series="1",
  pages="265--268",
  publisher="Brno University of Technology, Faculty of ERlectronic Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6029-4",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf"
}
Departments
Department of Radio Electronics (UREL)
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