In the campus driving scene, this paper proposes a general lane line detection algorithm with better comprehensive performance, that is, a local positioning detection method. First, a combination of classical graphics and a learning model based on a noise-tolerant recurrent neural network is used to complete the detection of the local area where the lane line is located, perform Hough transform and gray stretching on the grayscale image of the target lane line, design a recurrent neural network learning model, use the gradient data as a guide to train the algorithm model to learn in order to exclude distractors with similar gradient information and identify geometric attributes related of incomplete lane line morphology, and then complete the complementary work, apply sparse penalties, design recursive learning models with noise tolerance, and maximize the efficiency of using the self-built lane line image dataset with contaminated data annotation. Based on this, a deep reinforcement learning method is used to accurately locate the target lane line through six identification points, and based on the six accurate positioning points, the lane line is detected and drawn.