Traffic sign recognition using convolutional neural networks

Mutaqin Akbar

Abstract


Traffic sign recognition (TSR) can be used to recognize traffic signs by utilizing image processing. This paper presents traffic sign recognition in Indonesia using convolutional neural networks (CNN). The overall image dataset used is 2050 images of traffic signs, consisting of 10 kinds of signs. The CNN layer used in this study consists of one convolution layer, one pooling layer using maxpool operation, and one fully connected layer. The training algorithm used is stochastic gradient descent (SGD). At the training stage, using 1750 training images, 48 filters, and a learning rate of 0.005, the recognition results in 0.005 of loss and 100 % of accuracy. At the testing stage using 300 test images, the system recognizes the signs with 0.107 of loss and 97.33 % of accuracy.

Keywords


convolutional neural networks; traffic sign; sign recognition; image processing

References


K. Bengler, K. Dietmayer, B. Farber, M. Maurer, C. Stiller, and H. Winner, “Three decades of driver assistance systems: review and future perspectives,” IEEE Intelligent Transportation Systems Magazine, vol. 6, no. 4, pp. 6–22, 2014. doi: 10.1109/MITS.2014.2336271

V. K. Kukkala, J. Tunnell, S. Pasricha, and T. Bradley, “Advanced driver-assistance systems: a path toward autonomous vehicles,” IEEE Consumer Electronics Magazine, vol. 7, no. 5, pp. 18–25, 2018. doi: 10.1109/MCE.2018.2828440

A. Ziebinski, R. Cupek, D. Grzechca, and L. Chruszczyk, “Review of advanced driver assistance systems (ADAS),” in AIP Conference Proceedings, vol. 1906, 120002, 2017. doi: 10.1063/1.5012394

J. Levinson et al., “Towards fully autonomous driving: Systems and algorithms,” in 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, Jun. 2011, pp. 163–168. doi: 10.1109/IVS.2011.5940562

C. Rahmad, I. Fauziah Rahmah, and R. Andrie Asmara, “Deteksi dan pengenalan rambu lalu lintas di Indonesia menggunakan RGBN dan Gabor,” in Prosiding SENTRINOV, vol. 3, no. 1, pp. TI13-22, 2017.

T. O. Chrisdwianto, H. Fitriyah, and E. Rosana Widasari, “Perancangan sistem deteksi dan pengenalan rambu peringatan menggunakan metode template matching,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 3, pp. 1265–1274, 2018.

G. Romadhon and M. Murinto, “Aplikasi pengenalan citra rambu lalu lintas berbentuk lingkaran menggunakan metode jarak city-block,” Jurnal Sarjana Teknik Informatika, vol. 2, no. 2, pp. 286–294, 2014.

O. R. Sitanggang, H. Fitriyah, and F. Utaminingrum, “Sistem deteksi dan pengenalan jenis rambu lalu lintas menggunakan metode shape detection pada Raspberry Pi,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 12, pp. 6108–6117, 2018.

A. Triyadi and F. Utaminingrum, “Pengembangan sistem rekognisi rambu kecepataan menggunakan circle hough transform dan convolutional neural network berbasis Raspberry Pi,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 1, pp. 56–64, 2020.

X. Ying, “An Overview of overfitting and its solutions,” Journal of Physics: Conference Series, vol. 1168, 022022, 2019. doi: 10.1088/1742-6596/1168/2/022022

Y. Le Cun et al., “Handwritten digit recognition: applications of neural net chips and automatic learning,” in Neurocomputing, F. F. Soulié and J. Hérault, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990, pp. 303–318. doi: 10.1007/978-3-642-76153-9_35

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artificial Intelligent Review, vol. 53, no. 8, pp. 5455–5516, 2020. doi: 10.1007/s10462-020-09825-6

A. P. Engelbrecht, Computational intelligence: an introduction, 2nd ed. Chichester, England ; Hoboken, NJ: John Wiley & Sons, 2007.

S. W. Smith, The scientist and engineer’s guide to digital signal processing. California: California Technical Publishing, 1999.

D. Hutchison et al., “Evaluation of pooling operations in convolutional architectures for object recognition,” in Artificial Neural Networks – ICANN 2010, vol. 6354, K. Diamantaras, W. Duch, and L. S. Iliadis, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 92–101. doi: 10.1007/978-3-642-15825-4_10

P. Sadowski, “Notes on backpropagation.” 2016, Accessed: Oct. 15, 2020. [Online]. Available: https://www.ics.uci.edu/~pjsadows/notes.pdf

E. M. Dogo, O. J. Afolabi, N. I. Nwulu, B. Twala, and C. O. Aigbavboa, “A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks,” in 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), Belgaum, India, Dec. 2018, pp. 92–99, doi: 10.1109/CTEMS.2018.8769211




DOI: https://doi.org/10.14710/jtsiskom.2021.13959

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