Real-Time Vehicle Counting by Deep-Learning Networks

Chun Ming Tsai*, Frank Y. Shih, Jun Wei Hsieh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In order to improve the driving safety and reduce traffic congestion during holidays and work hours, a real-time vehicle detection and counting system is a very urgently needed system. In this paper, a lane-based vehicle counting system using deep-learning networks is proposed. Our method includes YOLO vehicle detection and lane-based vehicle counting. From the vehicle detection experimental results, YOLOv3-spp has the highest Precision, Recall, and F1 score, which achieve all 100% among three YOLOv3 methods and two YOLOv2 methods. From the vehicle counting experimental results, YOLOv3-608 has the highest Accuracy, Precision and F1 scores, which achieve 91.4%, 99.3%, and 95.3% among three YOLOv3 methods, two YOLOv2 methods, and one SSD method.

Original languageEnglish
Title of host publicationProceedings of 2022 International Conference on Machine Learning and Cybernetics, ICMLC 2022
PublisherIEEE Computer Society
Pages175-181
Number of pages7
ISBN (Electronic)9781665488327
DOIs
StatePublished - 2022
Event21st International Conference on Machine Learning and Cybernetics, ICMLC 2022 - Toyama, Japan
Duration: 9 Sep 202211 Sep 2022

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2022-September
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference21st International Conference on Machine Learning and Cybernetics, ICMLC 2022
Country/TerritoryJapan
CityToyama
Period9/09/2211/09/22

Keywords

  • Deep learning
  • Hsuehshan Tunnel
  • Vehicle counting
  • Vehicle detection

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