基于改进蚁群边缘检测的车道线检测算法

Lane detection algorithm based on improved ant colony edge detection

文章提出了一种基于改进蚁群边缘检测的车道线检测算法。文章使用一种基于细菌趋化性的蚁群优化边缘检测算法对灰度图像进行边缘提取,该算法能够得到更好的边缘连续性和清晰性。通过寻找边缘点最多的一行作为感兴趣区域(Region of Interest,ROI)的上界,经过Hough变换检测直线特征。过滤离群值后通过最小二乘法拟合出车道线。利用真实道路驾驶视频对车道检测算法进行仿真实验,实验结果表明本算法有较好的鲁棒性和抗干扰能力。

This paper proposes a lane detection algorithm based on improved ant colony edge detection. In this paper, a n ant colony optimization edge detection algorithm based on bacterial chemotaxis is used to extract the edge of gray image, which can achieve better edge continuity and clarity. By finding the line with the most edge points as the upper b ound of the region of interest(ROI), line features are detected through Hough transform. The lane line is fitted by l east square method after outlier filtering. The lane detection algorithm is simulated using real road driving videos. The experimental results show that the algorithm has good robustness and anti-interference ability.