Road Condition Classification Using Convolutional Neural Networks

  • G.-B. Maca Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania

Abstract

Autonomous driving is an increasingly important theme nowadays. One of the reasons behind this is the evolution of hardware components in the last years, which made possible both research and implementation of much more complex deep learning techniques. An interesting direction in the vast field of autonomous driving is the discrimination of the condition of the road, with respect to weather. This paper presents a supervised learning based approach to road condition classification. Specifically, we take advantage of the power of Convolutional Neural Networks (CNNs) in the context of image classification. We describe several CNN architectures that use state of the art deep learning techniques and compare their performance. In addition to the simple CNN-based learners, we propose a CNN-based ensemble learner able of a better predictive performance compared to the single models.

References

[1] M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316, 2016.
[2] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248–255. IEEE, 2009.
[3] F. Feng, L. Fu, and M. S. Perchanok. Comparison of alternative models for road surface condition classification. In TRB Annual Meeting, 2010.
[4] X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249–256, 2010.
[5] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
[6] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
[7] A. Karpathy. Convolutional neural networks: Architectures, convolution and pooling layers. 2016. last access (july, 2018).
[8] A. Karpathy. Softmax classifier. 2016. last access (july, 2018).
[9] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[10] A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
[11] A. Kuehnle and W. Burghout. Winter road condition recognition using video image classification. Transportation Research Record: Journal of the Transportation Research Board, (1627):29–33, 1998.
[12] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551, 1989.
[13] V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814, 2010.
[14] M. A. Nielsen. Neural networks and deep learning. 2015. last access (july, 2018).
[15] M. Nolte, N. Kister, and M. Maurer. Assessment of deep convolutional neural networks for road surface classification. arXiv preprint arXiv:1804.08872, 2018.
[16] R. Omer and L. Fu. An automatic image recognition system for winter road surface condition classification. In Intelligent transportation systems (itsc), 2010 13th international IEEE conference on, pages 1375–1379. IEEE, 2010.
[17] Y. Qian, E. J. Almazan, and J. H. Elder. Evaluating features and classifiers for road weather condition analysis. In Image Processing (ICIP), 2016 IEEE International Conference on, pages 4403–4407. IEEE, 2016.
[18] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[19] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014.
[20] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In AAAI, volume 4, page 12, 2017.
[21] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, et al. Going deeper with convolutions. Cvpr, 2015.
[22] M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In European conference on computer vision, pages 818–833. Springer, 2014.
Published
2019-12-07
How to Cite
MACA, G.-B.. Road Condition Classification Using Convolutional Neural Networks. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 64, n. 2, p. 14-33, dec. 2019. ISSN 2065-9601. Available at: <http://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/41>. Date accessed: 29 nov. 2020. doi: https://doi.org/10.24193/subbi.2019.2.02.
Section
Articles