Abstract:  Lane line detection is the prerequisite for the realization of lane keeping system and lane departure warning system. In order to further improve the accuracy of detection， combining deep learning and lane line detection， this paper proposes an improved SCNN lane line recognition algorithm. Based on the improved SCNN network， this method introduces PSA attention module， and combines it with VGG （Visual Geometry Group） network to propose a lane line recognition network called VGG-K. This network fuses context information， helps the message transmission between row and column pixels in each layer， enhances its recognition ability for continuous transformation targets， and then uses quadric model fitting to obtain the final lane line detection result. The improved model is tested on the dataset CULane. Training results show that the comprehensive evaluation index F1 value of the method reaches 92.1 in normal scenarios and 75.3 in harsh scenarios. Compared with other models， the detection ability of the proposed algorithm is significantly improved， and the proposed algorithm has a better recognition for lane lines under various complex conditions.

Key words: lane line detection, deep learning, target recognition, feature extraction