Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)

<Previous Article In Volume
Next Article In Volume>

Traffic Sign Detection Based on Deep Learning Methods

Authors
Xiqing Huang1, *, Fan Wang2, Shibin Yang3, Hongfei Zhang4
1Science in Information Technology, University of Technology Sydney, Sydney, 2007, Australia
2College of Information and Computer Engineering, Northeast Forestry University, Harbin, 150040, Heilongjiang, China
3Faculty of Engineering, Architecture, and Information Technology, University of Queensland, St Lucia, QLD, 4072, Australia
4School of Information Science and Engineering, Jinling College, Nanjing University, Nanjing, Jiangsu, China
*Corresponding author. Email: xiqing.huang1@student.uts.edu.au
Corresponding Author
Xiqing Huang
Available Online 23 December 2022.
DOI
10.2991/978-94-6463-034-3_72How to use a DOI?
Keywords
component; Deep learning; Traffic sign detection; CNN
Abstract

This article gives a brief overview of a few current research on traffic signs detection, which briefly reviews the concept and structure of traffic signs detection in the last decade. The methodology varies in different ways which is generally separated into two exact dimensions. The first one is the traditional method using the theory of computer vision with machine learning to detect the traffic signs, while the other one uses deep learning to train the model to detect the objects. In recent years, the methods based on deep learning have gradually replaced the traditional methods since they can extract features from traffic signs better and do predictions. Therefore, this paper mainly focuses on the deep learning methods for traffic signs detection and reviews previous work and their corresponding datasets and performance. The results based on different methods are compared. Finally, we made a conclusion based on this review.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
23 December 2022
ISBN
978-94-6463-034-3
ISSN
2589-4900
DOI
10.2991/978-94-6463-034-3_72How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xiqing Huang
AU  - Fan Wang
AU  - Shibin Yang
AU  - Hongfei Zhang
PY  - 2022
DA  - 2022/12/23
TI  - Traffic Sign Detection Based on Deep Learning Methods
BT  - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022)
PB  - Atlantis Press
SP  - 700
EP  - 708
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-034-3_72
DO  - 10.2991/978-94-6463-034-3_72
ID  - Huang2022
ER  -