Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

2020 | OriginalPaper | Chapter

Classification of Road Surface and Weather-Related Condition Using Deep Convolutional Neural Networks

Authors: Alexander Busch, Daniel Fink, Max-Heinrich Laves, Zygimantas Ziaukas, Mark Wielitzka, Tobias Ortmaier

Published in: Advances in Dynamics of Vehicles on Roads and Tracks

Publisher: Springer International Publishing

share
SHARE

Abstract

In order to achieve the goal of autonomous driving, a precise perception of the vehicle’s environment is required. In particular, the weather-related road condition has a major influence on vehicle dynamics and thus on driving safety.
In this paper, we compare Deep Convolutional Neural Networks of different computational effort, namely Inception-v3, GoogLeNet and the much smaller SqueezeNet, for classification of road surface and its weather-related condition. Previously, different regions of interest were compared in order to provide the networks with optimal input data.
Literature
1.
go back to reference Jarisa, W., Hartmann, B., Schönemann, B., Meister, T., Henze, R., Kücükay, F.: Road condition classification using information fusion. In: 7th International Munich Chassis Symposium 2016, pp. 939–957. Springer Vieweg (2017) Jarisa, W., Hartmann, B., Schönemann, B., Meister, T., Henze, R., Kücükay, F.: Road condition classification using information fusion. In: 7th International Munich Chassis Symposium 2016, pp. 939–957. Springer Vieweg (2017)
2.
go back to reference Wielitzka, M., Dagen, M., Ortmaier, T.: Sensitivity-based road friction estimation in vehicle dynamics using the unscented Kalman filter. In: Proceedings of the 2018 American Control Conference, Milwaukee, USA (2018) Wielitzka, M., Dagen, M., Ortmaier, T.: Sensitivity-based road friction estimation in vehicle dynamics using the unscented Kalman filter. In: Proceedings of the 2018 American Control Conference, Milwaukee, USA (2018)
3.
go back to reference Jokela, M., Kutila, M., Le, L.: Road condition monitoring system based on a stereo camera. In: Intelligent Computer Communication and Processing, pp. 423–428 (2009) Jokela, M., Kutila, M., Le, L.: Road condition monitoring system based on a stereo camera. In: Intelligent Computer Communication and Processing, pp. 423–428 (2009)
4.
go back to reference Yang, H.J., Jang, H., Kang, J.W., Jeong, D.S.: Classification algorithm for road surface condition. Int. J. Comput. Sci. Netw. Secur. 14(1), 1 (2014) Yang, H.J., Jang, H., Kang, J.W., Jeong, D.S.: Classification algorithm for road surface condition. Int. J. Comput. Sci. Netw. Secur. 14(1), 1 (2014)
5.
go back to reference Kawai, S., Takeuchi, K., Shibata, K., Horita, Y.: A method to distinguish road surface conditions for car-mounted camera images at night-time. In: 12th International Conference on ITS Telecommunications, pp. 668–672 (2012) Kawai, S., Takeuchi, K., Shibata, K., Horita, Y.: A method to distinguish road surface conditions for car-mounted camera images at night-time. In: 12th International Conference on ITS Telecommunications, pp. 668–672 (2012)
6.
go back to reference Amthor, M., Hartmann, B., Denzler, J.: Road condition estimation based on spatio-temporal reflection models. In: German Conference on Pattern Recognition, pp. 3–15. Springer (2015) Amthor, M., Hartmann, B., Denzler, J.: Road condition estimation based on spatio-temporal reflection models. In: German Conference on Pattern Recognition, pp. 3–15. Springer (2015)
7.
go back to reference Nolte, M., Kister, N., Maurer, M.: Assessment of deep convolutional neural networks for road surface classification. arXiv preprint. arXiv:​1804.​08872 (2018) Nolte, M., Kister, N., Maurer, M.: Assessment of deep convolutional neural networks for road surface classification. arXiv preprint. arXiv:​1804.​08872 (2018)
8.
go back to reference Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016) Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
9.
go back to reference Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015) Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
10.
go back to reference Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 mb model size. arXiv preprint. arXiv:​1602 (2016) Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5 mb model size. arXiv preprint. arXiv:​1602 (2016)
11.
go back to reference Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013) CrossRef Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013) CrossRef
12.
go back to reference Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 Year, 1000 km: the Oxford Robotcar dataset. Int. J. Robot. Res. 36(1), 3–15 (2016) CrossRef Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 Year, 1000 km: the Oxford Robotcar dataset. Int. J. Robot. Res. 36(1), 3–15 (2016) CrossRef
13.
go back to reference Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016) Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016)
14.
go back to reference Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., Darrell, T.: BDD100K: a diverse driving video database with scalable annotation tooling. CoRR. arXiv preprint. arXiv:​1805.​04687 (2018) Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., Darrell, T.: BDD100K: a diverse driving video database with scalable annotation tooling. CoRR. arXiv preprint. arXiv:​1805.​04687 (2018)
15.
go back to reference Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015) MathSciNetCrossRef Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015) MathSciNetCrossRef
Metadata
Title
Classification of Road Surface and Weather-Related Condition Using Deep Convolutional Neural Networks
Authors
Alexander Busch
Daniel Fink
Max-Heinrich Laves
Zygimantas Ziaukas
Mark Wielitzka
Tobias Ortmaier
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-38077-9_121

Premium Partner