Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 70, Issue -, Pages 895-903Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2017.11.026
Keywords
Driver assistance system; Road detection; Super-pixel; CNN; MRF
Categories
Funding
- National Natural Science Foundation of China [61601325]
- Key Technologies R & D Program of Tianjin [14ZCZDGX00033]
- research project for Application Foundation and Frontier Technology of Tianjin [14JCYBJC42300]
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Road detection aims at detecting the road surface ahead of the vehicle and plays a crucial role in driver assistance systems. To improve the accuracy and robustness of road detection approaches in complex environments, a new road detection method based on a convolutional neural network (CNN) and Markov random field (MRF) is proposed. The original road image is segmented into super-pixels of uniform size using the simple linear iterative clustering (SLIC) algorithm. On this basis, we train the convolutional neural network, which can automatically learn the features that are most beneficial to the classification. The trained convolutional neural network (CNN) is then applied to classify road and non road regions. Finally, based on the relationship between the super-pixel neighborhood, we utilize Markov random field (MRF) to optimize the classification results of the convolutional neural network (CNN). The approach provides the better performance. (C) 2017 Elsevier Ltd. All rights reserved.
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