Aiming at the poor adaptability of traditional lane recognition method in complex pavement, this paper proposes a lane recognition method based on full convolutional neural network and conditional random field, according to image segmentation technology. The method can make the neural network model identify the lanes by training a large amount of data, and then make the segmentation of the lanes' coverage and the lane edges more perfect through the conditional random field. At the same time, in order to solve the high requirement of real-time detection in expressway, a fully convolution neural network is designed in this paper. The network structure is simple with only 130000 parameters and three improvements are made as follows: BN algorithm is used to improve network generalization ability and convergence rate; LeakyReLU activation function is used to replace the commonly used relu or sigmoid activation function, and using Nadam as the network optimizer makes the network have better robustness; Conditional random field is used as the back-end processing solution insufficient lane segmentation and further to increase lane coverage. Finally, in order to solve the problem of complex road environment in urban road testing, this paper uses the back-end processing of FCN-16s network model and conditional random field to realize the recognition of complex urban roads. Experiments show that the network model designed in this paper is more real-time and sufficient for lane identification in the face of high-speed expressways and simple lanes. In the complex environment of urban road, FCN-16s model plus conditional random field can identify lane more accurately and get good results on KITTI road test benchmarks.
Lane recognition method based on fully convolution neural network and conditional random fields
First published at:Feb 18, 2019
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Supported by National Natural Science Foundation of China (61471154) and Anhui Science and Technology Research Project (170d0802181)
Get Citation: Ye Zihao, Sun Rui, Wang Huihui. Lane recognition method based on fully convolution neural network and conditional random fields[J]. Opto-Electronic Engineering, 2019, 46(2): 180274.
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