Real-time lane detection method based on semantic segmentation

Lane line recognition is an important task of automatic driving environment perception. In recent years, the deep learning method based on convolutional neural network has achieved good results in target detection and scene segmentation. Based on the idea of semantic segmentation, this paper designs a lightweight Lane segmentation network based on encoding and decoding structure. Aiming at the problem of large amount of computation of convolution neural network, the deep separable convolution is introduced to replace the ordinary convolution to reduce the amount of convolution computation. Moreover, a more efficient convolution structure of laneconv and lanedeconv is proposed to further improve the computational efficiency. Secondly, in order to obtain better lane line feature representation ability, in the coding stage, a dual attention mechanism module (CBAM) connecting spatial attention and channel attention in series is introduced to improve the accuracy of lane line segmentation. A large number of experiments are carried out on tusimple lane line data set. The results show that this method can significantly improve the lane line segmentation speed, and has a good segmentation effect and robustness under various conditions. Compared with the existing lane line segmentation models, the proposed method is similar or even better in segmentation accuracy, but significantly improved in speed.