CNN-Based Traffic Sign Recognition

DOI

10.2991/978-94-6463-094-7_16How to use a DOI?

Keywords

CNN; Traffic sign; Deep learning; Transfer learning; Image enhancement

Abstract

Traffic signs are a crucial part of maintaining driver and pedestrian safety on the road since they are being designed to provide essential information and alerts of potential hazards. With the rapid development of Advanced Driver Assistance Systems (ADAS), traffic sign recognition is also becoming much of a concern. However, due to real-world variations such as lighting conditions, occlusion, weather factors, motion blur and colour fading, there are still some failures in traffic sign recognition that cannot be perfectly resolved. Therefore, we implement image enhancement techniques and a pre-trained convolutional neural network for traffic sign recognition in this paper. Our proposed model uses the pre-trained VGG19 model as the baseline model and changes the fully connected layer and classifier of the VGG19 model. The experimental results demonstrate the effectiveness of applying image enhancement. Our proposed model was able to outperform the traditional machine learning method but did not surpass other deep learning methods.

Copyright

© 2022 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.

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TY  - CONF
AU  - Shin Wee Fiona Liou
AU  - Hau-Lee Tong
AU  - Kok-Why Ng
AU  - Hu Ng
PY  - 2022
DA  - 2022/12/27
TI  - CNN-Based Traffic Sign Recognition
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 195
EP  - 204
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_16
DO  - 10.2991/978-94-6463-094-7_16
ID  - Liou2022
ER  -