Real-Time Vehicle Counting by Deep-Learning Networks

Document Type

Conference Proceeding

Publication Date

1-1-2022

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.

Identifier

85142514378 (Scopus)

ISBN

[9781665488327]

Publication Title

Proceedings International Conference on Machine Learning and Cybernetics

External Full Text Location

https://doi.org/10.1109/ICMLC56445.2022.9941299

e-ISSN

21601348

ISSN

2160133X

First Page

175

Last Page

181

Volume

2022-September

Grant

108-2221-E-845 - 003 -MY3

Fund Ref

Ministry of Science and Technology, Taiwan

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