Abstract
Lane detection is a key technology in automatic driving systems. Aiming at the problems of low accuracy and susceptible to interference in the traditional detection algorithm as for yellow lane lines, and poor recognition of curved lane lines by the traditional Hough transform. In this paper, a lane line detection method based on machine vision is proposed. Firstly, the white lane lines are identified in the gray space, the yellow lane lines are identified in the HSV space and the yellow lane lines and white lane lines are then merged. The interference is reduced by dividing the region of interest and Gaussian filtering. Then, the edge of the lane line is identified by edge detection and the image is transformed into an aerial view by inverse perspective transformation. Finally, the sliding window polynomial fitting method is adopted to fit the lane lines. At the same time, a lane departure warning method based on calculating curve curvature and center deviation is proposed.
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