Lane detection method based on row and column guidance

The lane detection algorithm based on deep learning classifies according to different lane representation forms. Among them, the lane detection algorithm based on row classification uses the prior characteristics of lane distribution to realize fast and accurate lane detection. With the development of technology, the recall rate and detection ability of row classification lane detection algorithm in complex urban scenes are slightly insufficient. This paper reconstructs the lane line detection method based on row classification, and detects the exact position of lane lines by calculating the positive sample grid of lane lines, so as to replace the expectation-based lane line location calculation method; Secondly, combining the prior characteristics of lane lines, the column guidance matrix is designed to solve the problem of insufficient constraints caused by the sparse distribution of lane lines between different anchors at the edge of the image. At the same time, the global information of the guidance matrix is used to improve the lane detection ability of the model in the complex urban environment. In this paper, CULane and TuSimple benchmark data sets were used for verification, and the F1 reached 77.9% and 97.2% respectively, which realized the fast and accurate detection of lane lines in complex environments.