| 吕侃徽,张大兴.基于改进 Hough 变换耦合密度空间聚类的车道线检测算法[J].电子测量与仪器学报,2020,34(12):172-180 |
| 基于改进 Hough 变换耦合密度空间聚类的车道线检测算法 |
| Lane detection algorithm based on improved hough transform coupled density space clustering |
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| DOI： |
| 中文关键词: 车道线检测 Hough 变换 密度空间聚类 边缘像素梯度 曲线拟合 消失点 |
| 英文关键词:lane detection Hough transform density space clustering edge pixel gradient curve fitting vanishing point |
| 基金项目:国家自然科学基金(61272391, 61572160)、浙江省自然科学基金(LY20F020002)、浙江省科技厅科研项目(15ZJSS1024)资助 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| In order to improve the accuracy and robustness of lane detection, as well as reduce the influence of illumination change and
background 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 are
represented by the least square method. Secondly, the improved Hough transform was used to detect the small line segments in the
image. A noisy density based spatial clustering of applications with noise was introduced to cluster the extracted small segments, filter
out the redundancy and noise in the image, and retain the key information of lane boundary. Then, the gradient direction of the edge
pixels was used to define the direction of the small line segments, so that the small line segments on the same side of the boundary have
the same direction, while the two small line segments on the opposite lane boundary have the opposite direction. Through the direction
function of the small line segments, the candidate clusters of the lane segments were obtained. Finally, according to the candidate
clusters, 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 that
compared with the current popular lane line detection algorithm, under the bad factors such as illumination change and background
interference, this algorithm presents more ideal accuracy and robustness, which can accurately identify the normal lane line. |
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