Abstract
Road detection aims at detecting the (drivable) road surface ahead vehicle and plays a crucial role in driver assistance system. To improve the accuracy and robustness of road detection approaches in complex environments, a new road detection method based on CNN (convolutional neural network) and MRF (markov random field) is proposed. The original road image is segmented into super-pixels of uniform size using simple linear iterative clustering (SLIC) algorithm. On this basis, we train the CNN which can automatically learn the features that are most beneficial to classification. Then, the trained CNN is applied to classify road region and non-road region. Finally, based on the relationship between the super-pixels neighborhood, we utilize MRF to optimize the classification results of CNN. Quantitative and qualitative experiments on the publicly datasets demonstrate that the proposed method is robust in complex environments. Furthermore, compared with state-of-the-art algorithms, the approach provides the better performance.
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Acknowledgements
This work is supported by National Natural Science Foundation of China under grant No. 61601325, the key technologies R & D program of Tianjin under grant No.14ZCZDGX00033, and research project for application foundation and frontier technology of Tianjin under grant No. 14JCYBJC42300.
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Geng, L., Sun, J., Xiao, Z., Zhang, F., Wu, J. (2018). Combining CNN and MRF for Road Detection. In: Lu, H., Xu, X. (eds) Artificial Intelligence and Robotics. Studies in Computational Intelligence, vol 752. Springer, Cham. https://doi.org/10.1007/978-3-319-69877-9_12
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DOI: https://doi.org/10.1007/978-3-319-69877-9_12
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