Lane detection plays an important role in intelligent transportation.The accurate and fast lane detection is important for assisted driving and automatic driving.In view of the poor accuracy and slow speed of deep learning methods for lane line recognition,a method abbreviated as LaneSegNet is proposed for efficient lane line segmentation.First,based on the principle of encoding and decoding network,a backbone network Lane-Net is constructed to extract the lane line features and segment the lane lines.Then,the multi-scale dilated convolution feature fusion network is used to greatly expand the receptive field of the model and extract the global features.Finally,the 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% on TuSimple dataset,while on the CULane dataset,the detection accuracies are 92.5% and 75.2% for standard pavement and multiple pavements,respectively.Compared with other models,the proposed LaneSegNet has better segmentation accuracy and reasoning speed,and has stronger adaptability and robustness.