A lane line detection algorithm based on vanishing points and color filters was proposed to address the problems of poor practicality of the classical Hough lane line detection method its inability to accurately distinguish between lane lines and road edges and the simplicity of the applied road scenes. The proposed algorithm not only improves the accuracy of lane line detection but also enables the application of more complex driving scenarios. First five consecutive frames of the driving video were pre-processed to obtain the location of the vanishing point of the lane lines in the driving environment and the Region of Interest ROI of the driving environment image could be selected adaptively. Then the ROI image was filtered according to the lane line color feature to obtain a binary image and data such as the centroid and tilt angle of all connected areas in the binary image was obtained. By combining the vanishing point feature and angle threshold to limit the data in the connected area conforming to the lane line feature was screened and recorded. The data of the area was then divided into a larger area to obtain more centroid points and the area centroid points that were missed and met the characteristics of the lane line were identified. Finally the least squares method was used to fit the obtained centroid points and identify the lane lines. The experiment shows that the algorithm can quickly and accurately detect lane lines on multi-scene roads. Comparing the classical Hough algorithm the algorithm has certain robustness and real-time performance.