Publication result detail

Traffic Analysis Using Machine Learning Approach

ZELENÝ, O.; FRÝZA, T.

Original Title

Traffic Analysis Using Machine Learning Approach

English Title

Traffic Analysis Using Machine Learning Approach

Type

Paper in proceedings (conference paper)

Original 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.

English 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

Key words in English

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

Authors

ZELENÝ, O.; FRÝZA, T.

RIV year

2024

Released

26.04.2022

Publisher

Brno University of Technology, Faculty of ERlectronic Engineering and Communication

Location

Brno

ISBN

978-80-214-6029-4

Book

PROCEEDINGS I OF THE 28TH STUDENT EEICT 2022 General papers

Edition

1

Pages from

265

Pages to

268

Pages count

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf

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"
}