基于机器视觉的车道线精确检测算法

车道线检测是车辆智能驾驶系统的重要组成部分.针对传统的车道线检测方法精度低、实时性能差的问题,提出一种基于机器视觉的车道线精确检测算法.该算法采用车道内侧边缘线代表车道线,具体包括预处理和车道线提取两个步骤:预处理部分包括灰度化、Sobel边缘检测、ROI设定、二值化,最终得到车道线部分的二值图像;车道线提取部分包括图像切片、改进的Hough直线检测、DBSCAN直线聚类以及直线拟合,最终得到精确的车道边缘线信息.最后将算法应用于各种场景下的路况测试,实验结果表明:该算法的平均准确率为94.9％,平均处理时长为25.6 ms/f,具有很好的实时性和鲁棒性.

Accurate Detection Algorithm of Lane Line Based on Machine Vision

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, an accurate lane line detection algorithm is proposed 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. The algorithm mainly includes two parts:preprocessing and lane line extraction. The preprocessing part includes grayscale, Sobel edge detection, region of interest setting and 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 and straight line fitting, and finally accurate lane edge line information is obtained. 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.