Abstract
In autonomous driving, the precise detection of traffic signs is crucial. Accurate detection can dramatically reduce problems such as road accidents involving autonomous vehicles. Numerous algorithms and libraries have been developed to implement reliable and safe detection of traffic signs. In this project, we aim to examine the performance of two advanced neural network models, namely, convolutional neural networks and recurrent neural networks, and choose the most suitable one in terms of resistance and effectiveness for this particular application. This paper presents the simulations carried out and the results obtained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
J. Ondruš, E. Kolla, P. Vertaľ, Ž. Šarić, How do autonomous cars work? Transp. Res. Procedia 44, 226–233 (2020)
A. Gupta, A. Anpalagan, L. Guan, A.S. Khwaja, Deep learning for object detection and scene perception in self-driving cars: survey, challenges, and open issues. Array 100057 (2021)
N.G.S.S. Srinath, A.Z. Joseph, S. Umamaheswaran, C.L. Priyanka, M. Nair, P. Sankaran, NITCAD-developing an object detection, classification and stereo vision dataset for autonomous navigation in Indian roads. Procedia Comput Sci 171, 207–216 (2020)
E. Khatab, A. Onsy, M. Varley, Vulnerable objects detection for autonomous driving: a review. Integration (2021)
A. Vennelakanti, S. Shreya, R. Rajendran, D. Sarkar, D. Muddegowda, P. Hanagal, Traffic sign detection and recognition using a CNN ensemble, in 2019 IEEE international conference on consumer electronics (ICCE) (2019), (pp. 1–4). IEEE
Z. Liu, J. Du, F. Tian, J. Wen, MR-CNN: a multi-scale region-based convolutional neural network for small traffic sign recognition. IEEE Access 7, 57120–57128 (2019)
A. Bouti, Mahraz, M. A., Riffi, J., Tairi, H., Road sign recognition with Convolutional Neural Network, in 2018 International Conference on Intelligent Systems and Computer Vision (ISCV) (2018), (pp. 1–7). IEEE
W.A. Haque, S. Arefin, A.S.M. Shihavuddin, M.A. Hasan, DeepThin: a novel lightweight CNN architecture for traffic sign recognition without GPU requirements. Expert Syst. Appl. 168, 114481 (2021)
T. Nguyen, G. Nguyen, B.M. Nguyen, EO-CNN: an enhanced CNN model trained by equilibrium optimization for traffic transportation prediction. Procedia Comput. Sci. 176, 800–809 (2020)
F. Zanetti. Convolutional networks for traffic sign classification. MS thesis. (2016)
T.Y. Kim, S.B. Cho, Web traffic anomaly detection using C-LSTM neural networks. Expert Syst. Appl. 106, 66–76 (2018)
W. Yin, K. Kann, M. Yu, H. Schütze, Comparative study of CNN and RNN for natural language processing. arXiv preprint arXiv:1702.01923 (2017)
S. Albawi, T.A. Mohammed, S. Al-Zawi, Understanding of a convolutional neural network, in 2017 International Conference on Engineering and Technology (ICET), (2017), (pp. 1–6). Ieee
A.N. Michel, Recurrent neural networks: overview and perspectives, in Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS'03. (Vol. 3, pp. III–III). (2003), IEEE
A. Bouti, M.A. Mahraz, J. Riffi, H. Tairi, A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network. Soft Comput., 1–13 (2019)
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Proces. Syst. 25, 1097–1105 (2012)
J. Schmidhuber, S. Hochreiter, Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bouti, A., Mahraz, M.A., Riffi, J., Tairi, H. (2022). Traffic Sign Detection: A Comparative Study Between CNN and RNN. In: Ouaissa, M., Boulouard, Z., Ouaissa, M., Guermah, B. (eds) Computational Intelligence in Recent Communication Networks . EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77185-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-77185-0_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77184-3
Online ISBN: 978-3-030-77185-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)