Traffic Sign Classification Using Convolutional Neural Network

Authors

  • Pranav Kale  Computer Engineering, VIIT, Pune, Maharashtra, India
  • Mayuresh Panchpor  Computer Engineering, VIIT, Pune, Maharashtra, India
  • Saloni Dingore  Computer Engineering, VIIT, Pune, Maharashtra, India
  • Saloni Gaikwad  Computer Engineering, VIIT, Pune, Maharashtra, India
  • Prof. Dr. Laxmi Bewoor  Computer Engineering, VIIT, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT217545

Keywords:

GTSRB dataset, Classify Traffic Sign, Convolutional Neural Network

Abstract

In today's world, deep learning fields are getting boosted with increasing speed. Lot of innovations and different algorithms are being developed. In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment. Different algorithms were developed to classify these Signs. But performance still needs to improve for real time environment. Even the computational power required to train such model is high. In this paper, Convolutional Neural Network model is used to Classify Traffic Sign. The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle. This proposed model is able to outperform previous models and resulted with accuracy of 99.6% on validation set. This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper. [24]

References

  1. J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 14531460. 2011.
  2. Sermanet, P., LeCun, Y. Traffic sign recognition with multi-scale convolutional networks. In Neural Networks (IJCNN), The 2011 International Joint Conference on (pp. 2809-2813). IEEE (2011)
  3. Haloi, M. A novel pLSA based Traffic Signs Classification System. arXiv preprint arXiv:1503.06643 (2015).
  4. Zaklouta, F., Stanciulescu, B., Hamdoun, O. Traffic sign classification using kd trees and random forests. In Neural Networks (IJCNN), The 2011 International Joint Conference on (pp. 2151-2155). IEEE (2011)
  5. Diederik P. Kingma, Jimmy Ba, Adam: A Method for Stochastic Optimization, 3rd International Conference for Learning Representations, San Diego, 2015 , arXiv:1412.6980 cs.LG]
  6. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... Rabinovich, A. Going deeper with convolutions. arXiv preprint arXiv:1409.4842 (2014)
  7. Ioffe, S., Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
  8. He, K., Zhang, X., Ren, S., Sun, J. Delving deep into rectifiers : Surpassing human-level performance on imagenet classification. arXiv preprint arXiv:1502.01852 (2015)
  9. Lai, Yan & Wang, Nanxin & Yusi, Yang & Lin, Lan. (2018). Traffic Signs Recognition and Classification based on Deep Feature Learning. 622-629. 10.5220/0006718806220629.
  10. D. Cireşan, U. Meier, J. Masci and J. Schmidhuber, "A committee of neural networks for traffic sign classification," The 2011 International Joint Conference on Neural Networks, San Jose, CA, 2011, pp. 1918-1921, doi: 10.1109/IJCNN.2011.6033458.
  11. Zhang, J., Wang, W., Lu, C. et al. Lightweight deep network for traffic sign classification. Ann. Telecommun. 75, 369–379 (2020). https://doi.org/10.1007/s12243-019-00731-9
  12. F. Jurišić, I. Filković and Z. Kalafatić, "Multiple-dataset traffic sign classification with OneCNN," 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), Kuala Lumpur, 2015, pp. 614-618, doi: 10.1109/ACPR.2015.7486576.
  13. Dan Cireşan, Ueli Meier, Jonathan Masci, Jürgen Schmidhuber, Multi-column deep neural network for traffic sign classification, Neural Networks, Volume 32, 2012, Pages 333-338, ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2012.02.023.
  14. Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118
  15. http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
  16. https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148
  17. Traffic sign recognition and classification with modified residual networks - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/The-total-43-classes-in-GTSRB-From-top-to-bottom-there-are-four-categories_fig1_322945549 accessed 26 Nov, 2020]
  18. An Efficient Traffic Sign Recognition Approach Using a Novel Deep Neural Network Selection Architecture: Proceedings of IEMIS 2018, Volume 3 - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Overview-of-the-GTSRB-Dataset_fig1_327389916 accessed 26 Nov, 2020]
  19. https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/
  20. https://www.baeldung.com/cs/ml-relu-dropout-layers
  21. Valueva, M.V.; Nagornov, N.N.; Lyakhov, P.A.; Valuev, G.V.; Chervyakov, N.I. (2020). "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation". Mathematics and Computers in Simulation. Elsevier BV. 177: 232-243 doi: 10.1016/j.matcom.2020.04.031. ISSN 0378-4754.
  22. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1 (January 2014), 1929–1958.
  23. Zhilu Zhang, Mert R. Sabuncu, Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels 32nd Conference on Neural Information Processing Systems (NeurIPS 2018)., arXiv:1805.07836 cs.LG]
  24. Bewoor, Laxmi A.; Kale, Pranav and Panchpor, Mayuresh “A STREAMLINE TRAFFIC SIGN CLASSIFICATION SYSTEM UTILIZING CONVOLUTIONALNEURAL NETWORK MODEL” AUSTRALIAN Patent, 2021101273, April21, 2021.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-12-30

Issue

Volume 7, Issue 6, November-December-2021

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Research Articles

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How to Cite

[1]
Pranav Kale, Mayuresh Panchpor, Saloni Dingore, Saloni Gaikwad, Prof. Dr. Laxmi Bewoor, " Traffic Sign Classification Using Convolutional Neural Network" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 6, pp.01-10, November-December-2021. Available at doi : https://doi.org/10.32628/CSEIT217545