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. doi: 10.12086/oee.2019.180274 |
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Overview: In recent years, with the rapid development of smart cars, autonomous driving has attracted great attention from industry and academia. Lane recognition is a fundamental work for achieving automatic driving. Specifically, accurate lane recognition not only allows the vehicle to travel on the correct road but also alerts the control system with other information such as lane markings, pedestrians and other anomalous events. The traditional lane detection method generally adopts the steps of preprocessing, edge detection, Hough transform, lane matching, lane segmentation, etc. These methods interact with each other, and it is difficult to achieve global optimization and real-time optimization simultaneously. Furthermore, the lane change adaptation is obviously insufficient. Deep neural network is a powerful visual analysis tool. Compared with the traditional shallow computing structure, its main advantage lies in its self-learning ability according to input data and its end-to-end unified structure. 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 result on KITTI road test benchmarks.
Schematic diagram of the full convolutional neural network
VGG16 model diagram
Lane recognition process
Full convolutional neural network structure
Straight road comparison. (a) FCN; (b) FCN+CRF; (c) Real scene
Corrupted road comparison. (a) FCN; (B) FCN+CRF; (c) Real scene
Curve road comparison. (a) FCN; (B) FCN+CRF; (c) Real scene
Lane line labeling under the lane line data set
UU_ROAD_000020 detection comparison. (a) FCN-LC; (b) ANN; (c) BM; (d) FCN+CRF
Detection comparison of 10 UU_ROAD_000082. (a) FCN-LC; (b) ANN; (c) BM; (d) FCN+CRF