Chongqing University of Posts and Telecommunications
At present, lane line detection is an important part of the vehicle''s intelligent driving system. Aiming at the problems of low accuracy and poor real-time performance in traditional lane line detection methods, this paper proposes an accurate lane line detection algorithm based on machine vision. The algorithm uses the inner edge line of the lane to represent the lane line, which improves the accuracy and real-time performance compared with the traditional algorithm. The algorithm mainly includes two parts: preprocessing and lane line extraction; the preprocessing part includes grayscale, Sobel edge detection, region of interest setting, binarization, and finally the binary image of the lane line part is obtained; the lane line extraction part includes the image Slicing, improved Hough line detection, DBSCAN line clustering, straight line fitting, and finally get accurate lane edge line information. Finally, the algorithm is applied to road condition tests in various scenarios. The experimental results show that the average accuracy of the algorithm is 94.9%, and the average processing time pre frame is 25.6ms. The algorithm has good real-time and robustness.