Overview: In recent years, the rapid development of neural network has greatly improved the efficiency of lane detection. However, convolutional neural network has become a new problem restricting the development of lane detection because of its large amount of calculation and high hardware requirements. Lane detection methods based on deep learning can be divided into two categories: detection based methods and segmentation based methods. The method based on detection has the advantages of high speed and strong ability to deal with straight lane. However, when the environment is complex and there are many curves, the detection effect is obviously not as good as the segmentation based method. This paper adopts the segmentation based method, and considers that the performance of lane detection can be improved by establishing global context correlation and enhancing the effective expression of important Lane feature channels. Attention mechanism is a model that can significantly improve network performance. It imitates the human visual processing mechanism, strengthens the attention to important information, so as to reasonably allocate network resources and improve the detection efficiency and accuracy of the network. Therefore, this paper uses the CBAM model. In this model, channel attention and spatial attention are serial to obtain better feature representation ability. Spatial attention learns the positional relationship between lane line pixels, and channel attention learns the importance of different channel features. In addition, in order to solve the problem of complex convolution calculation and slow running speed based on segmentation model, a more efficient convolution structure is proposed to improve the computational efficiency. A new fast down sampling module laneconv and a new fast up sampling module laneconv are introduced, and the depth separable convolution is introduced to further reduce the amount of calculation. They are located in the coding part of the network. The decoding part outputs the binary segmentation result. Then, the results are clustered by DBSCAN to obtain the lane line. After clustering, compared with the complex post-processing in other literature, this paper only uses simple cubic fitting to fit the lane line, which further improves the speed. Therefore, the running speed of the model proposed in this paper is better than most segmentation based methods. Finally, a large number of experiments are carried out on tusimple Lane database. The results show that the method has good robustness under various road conditions, especially in the case of occlusion. Compared with the existing models, it has comprehensive advantages in detection accuracy and speed.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.