基于改进Hough变换耦合密度空间聚类的车道线检测算法

为了提高车道线检测的准确性与鲁棒性,降低光照变化与背景干扰的影响,提出了一种改进的Hough变换耦合密度空间聚类的车道线检测算法。首先,建立车道线模型,将车道边界分解为一系列的小线段,借助最小二乘法来表示车道线中的线段。再利用改进的Hough变换对图像中的小线段进行检测。引入具有密度空间聚类方法(density based spatial clustering of applications with noise, DBSCAN),对提取的小线段进行聚类,过滤掉图像中的冗余和噪声,同时保留车道边界的关键信息。随后,利用边缘像素的梯度方向来定义小线段的方向,使得边界同一侧的小线段具有相同的方向,而位于相反车道边界的两个小线段具有相反的方向,通过小线段的方向函数得到车道线段候选簇。最后,根据得到的小线段候选簇,利用消失点来拟合最终车道线。在Caltech数据集与实际道路中进行测试,数据表明:与当前流行的车道线检测算法相比,在光照变化、背景干扰等不良因素下,所以算法呈现出更理想的准确性与稳健,可准确识别正常车道线。

Lane detection algorithm based on improved hough transform coupled density space clustering

In order to improve the accuracy and robustness of lane detection, as well as reduce the influence of illumination change andbackground interference, an improved Hough transform coupling density space clustering algorithm for lane line detection was proposed.Firstly, the lane line model is established, and the lane boundary is decomposed into a series of small line segments, which arerepresented by the least square method. Secondly, the improved Hough transform was used to detect the small line segments in theimage. A noisy density based spatial clustering of applications with noise was introduced to cluster the extracted small segments, filterout the redundancy and noise in the image, and retain the key information of lane boundary. Then, the gradient direction of the edgepixels was used to define the direction of the small line segments, so that the small line segments on the same side of the boundary havethe same direction, while the two small line segments on the opposite lane boundary have the opposite direction. Through the directionfunction of the small line segments, the candidate clusters of the lane segments were obtained. Finally, according to the candidateclusters, the vanishing point was used to fit the final lane line. It was tested in Caltech data set and the actual road, the data shows thatcompared with the current popular lane line detection algorithm, under the bad factors such as illumination change and backgroundinterference, this algorithm presents more ideal accuracy and robustness, which can accurately identify the normal lane line.