To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline

Authors

  • Riadh Ayachi Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Tunisia
  • Yahia ElFahem Said 1Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Tunisia. 2Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia
  • Mohamed Atri Laboratory of Electronics and Microelectronics (EμE), Faculty of Sciences of Monastir, University of Monastir, Tunisia

DOI:

https://doi.org/10.30564/aia.v1i1.569

Abstract

Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has successfully used to solve computer vision problems because of its methodology in processing images that are similar to the human brain decision making. The evaluation of the proposed pipeline was trained and tested using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real time application.

Keywords:

Traffic signs classification, Autonomous vehicles, Artificial intelligence, Deep learning, Convolutional Neural Networks CNN, Image understanding

References

[1] O'Shea, K., & Nash, R. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458, 2015.

[2] Ciresan, D. C., Meier, U., Masci, J., Maria Gambardella, L., & Schmidhuber, J. Flexible, high performance convolutional neural networks for image classification. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 2011,22(1): 1237.

[3] Tompson, J., Goroshin, R., Jain, A., et al. Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 648-656.

[4] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[5] Simard, P. Y., Steinkraus, D., & Platt, J. C. Best practices for convolutional neural networks applied to visual document analysis. In null (p. 958). IEEE, 2003.

[6] LeCun, Y., Jackel, L. D., Bottou, L., Cortes, C., Denker, J. S., Drucker, H., ... & Vapnik, V. Learning algorithms for classification: A comparison on handwritten digit recognition. Neural networks: the statistical mechanics perspective, 1995, 261: 276.

[7] Zhu, H., Chan, F. H., & Lam, F. K. Image contrast enhancement by constrained local histogram equalization. Computer vision and image understanding, 1999, 73(2): 281-290.

[8] Kim, J. Y., Kim, L. S., & Hwang, S. H. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE transactions on circuits and systems for video technology, 2001, 11(4): 475-484.

[9] Stark, J. A. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on image processing, 2000, 9(5): 889-896.

[10] He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016: 770-778.

[11] Sercu, T., Puhrsch, C., Kingsbury, B., & LeCun, Y. Very deep multilingual convolutional neural networks for LVCSR. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, IEEE, 2016: 4955-4959.

[12] U.S. vehicle deaths topped 40,000 in 2017, National Safety Council estimates. https://www.usatoday.com/story/money/cars/2018/02/15/national-safety-council-traffic-deaths/340012002

[13] Vitabile, S.; Pollaccia, G.; Pilato, G.; Sorbello, F. Road signs recognition using a dynamic pixel aggregation technique in the HSV color space. In Proceedings of the 11th International Conference on Image Analysis and Processing, Palermo, Italy, 2001, 26–28: pp. 572–577.

[14] Zeng, Y.; Lan, J.; Ran, B.; Wang, Q.; Gao, J. Restoration of motion-blurred image based on border deformation detection: A traffic sign restoration model. PLoS ONE, 10, e0120885. 2015.

[15] Ohgushi, K.; Hamada, N. Traffic sign recognition by bags of features. In Proceedings of the TENCON 2009—2009 IEEE Region 10 Conference, Singapore, 2009, 23–26: 1–6.

[16] Wu, J.; Si, M.; Tan, F.; Gu, C. Real-time automatic road sign detection. In Proceedings of the Fifth International Conference on Image and Graphics (ICIG ’09), Xi’an, China, 2009: 540– 544.

[17] Belaroussi, R.; Foucher, P.; Tarel, J.P.; Soheilian, B.; Charbonnier, P.; Paparoditis, N. Road sign detection in images: A case study. In Proceedings of the 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey; 2010: 484–488.

[18] Shoba, E.; Suruliandi, A. Performance analysis on road sign detection, extraction and recognition techniques. In Proceedings of the 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT), Nagercoil, India, 2013: 1167–1173.

[19] Wali, S.B.; Hannan, M.A.; Hussain, A.; Samad, S.A. An automatic traffic sign detection and recognition system based on colour segmentation, shape matching, and svm. Math. Probl. Eng, 2015.

[20] Lai, C.H.; Yu, C.C. An efficient real-time traffic sign recognition system for intelligent vehicles with smart phones. In Proceedings of the 2010 International Conference on Technologies and Applications of Artificial Intelligence, Hsinchu, Taiwan, 2010: 195–202.

[21] Virupakshappa, K.; Han, Y.; Oruklu, E. Traffic sign recognition based on prevailing bag of visual words representation on feature descriptors. In Proceedings of the 2015 IEEE International Conference on Electro/Information Technology (EIT), Dekalb, IL, USA, 2015: 489–493.

[22] Shams, M.M.; Kaveh, H.; Safabakhsh, R. Traffic sign recognition using an extended bag-of-features model with spatial histogram. In Proceedings of the 2015 Signal Processing and Intelligent Systems Conference (SPIS), Tehran, Iran, 2015: 189–193.

[23] Lin, C.-C.; Wang, M.-S. Road sign recognition with fuzzy adaptive pre-processing models. Sensors, 6415. 2012.

[24] Yin, S.; Ouyang, P.; Liu, L.; Guo, Y.; Wei, S. Fast traffic sign recognition with a rotation invariant binary pattern-based feature. Sensors, 2015, 2161–2180.

[25] Rachmadi½, R. F., Komokata½, Y., Íchimura½, K., & Koutaki½, G. (2017). Road sign classification system using cascade convolutional neural network, 2017.

[26] Continental. Traffic Sign Recognition. Available online: 2017. http://www.contionline.com/generator/www/de/en/continental/automotive/general/chassis/safety/hidden/verkehrszeichenerkennung_en.html

[27] Choi, Y.; Han, S.I.; Kong, S.-H.; Ko, H. Driver status monitoring systems for smart vehicles using physiological sensors: A safety enhancement system from automobile manufacturers. IEEE Signal Process. 2016: 22–34.

[28] Dean, J., & Ghemawat, S. MapReduce: simplified data processing on large clusters. Communications of the ACM, 2008, 51(1), 107-113.

[29] Kim, J. Y., Kim, L. S., & Hwang, S. H. An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE transactions on circuits and systems for video technology, 2001, 11(4), 475-484.

[30] Abdullah-Al-Wadud, M., Kabir, M. H., Dewan, M. A. A., & Chae, O. A dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 2007, 53(2).

[31] Olga, R., Jia, D., Hao S., Jonathan, K., Sanjeev, S., Sean, M., Zhiheng, H., Andrej, K., Aditya, K., Michael, B., Alexander, C. B., and Li, F. ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.

[32] Hinton, Geoffrey E, Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.

[33] J. Stallkamp, M. Schlipsing, J. Salmen, C. Igel, Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition, Neural Networks. 2012, ISSN 0893-6080.http://www.sciencedirect.com/science/article/pii/S0893608012000457

[34] Radu Timofte, Karel Zimmermann, Luc van Gool, Multi-view traffic sign detection, recognition, and 3D localisation, IEEE Workshop on Applications of Computer Vision, WACV, 2009.

[35] Fredrik, L. and Michael, F., Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition, In Proceedings of the 17th Scandinavian Conference on Image Analysis, SCIA, LNCS 6688, 2011: 238-24.

[36] CireşAn, Dan, et al. "Multi-column deep neural network for traffic sign classification." Neural networks 2012, 32: 333-338.

[37] Gecer, B., Azzopardi, G., & Petkov, N. Color-blob-based COSFIRE filters for object recognition. Image and Vision Computing, 2017, 57: 165-174.

[38] Sermanet, P., & LeCun, Y. Traffic sign recognition with multi-scale convolutional networks. In Neural Networks (IJCNN), the 2011 International Joint Conference on. IEEE, 2011: 2809-2813.

Downloads

How to Cite

Ayachi, R., Said, Y. E., & Atri, M. (2019). To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline. Artificial Intelligence Advances, 1(1), 1–10. https://doi.org/10.30564/aia.v1i1.569

Issue

Vol. 1 , Iss. 1 (April 2019)

Article Type

Articles

License

Copyright © 2019 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC