Combining CNN and MRF for road detection

Abstract

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.