Lane detection plays an important role in the field of intelligent transportation. Its detection accuracy and speed have an important impact on assisted driving and automatic driving. Aiming at the problems of poor accuracy and slow speed of lane line recognition by deep learning methods, an efficient lane line segmentation method LaneSegNet is proposed. Firstly, based on the principle of encoding and decoding network, a backbone network Lane net is constructed to extract the lane line feature information and segment the lane line; Then, the multi-scale divided convolution feature fusion network is used to greatly expand the receptive field of the model and extract the global feature information; Finally, hybrid attention network is used to obtain rich lane line features and enhance the information related to the current task. The experimental results show that the accuracy of this method is 97.6% in TuSimple data set; On the CULane dataset, the detection accuracy of this method on standard pavement is 92.5%, and the comprehensive detection accuracy of multiple pavement is 75.2%. The LaneSegNet lane line detection method proposed in this paper has better segmentation accuracy and reasoning speed than other models, and has stronger adaptability and robustness.