基于图模型的道路检测方法

Road Detection Method Based on Graph Model

为解决城市环境低速移动机器人的道路检测问题,提出一种基于图模型的道路检测方法.首先,将道路图像划分为子图,计算子图特征向量,生成图模型结点集.然后,提出相近半径概念,计算相近结点边权值,生成图模型边集.在此基础上,采用基于最小生成树的结点合并规则对图模型结点进行合并,实现道路图像分割.最后,通过设置取样窗口,提取道路结点,分割出道路区域.通过实验分析道路检测精度与子图尺寸及阈值参数间的关系,研究采用灰度特征进行道路检测的可行性.实验结果表明,该方法能有效检测出不同类型道路图像中的道路区域,适用于道路检测.

To solve the detection problem for low-speed mobile robots in urban environments, a road detection approach based on graph model is proposed to detect road region. Firstly, the road image is segmented into sub-images, and the feature vector of each sub-image is computed to generate node set of the graph model. Then, the concept of neighbor radius for nodes is proposed to calculate edge weights between adjacent nodes, from which the edge set of graph is acquired. The graph nodes merging rule based on the minimum spanning tree algorithm is used to combine nodes, which realizes the road image segmentation. Finally, the road nodes are extracted to segment the road region by setting a node sample window. The relationship among the road detection precision, sub-image size and threshold parameter is studied in the experiment. The feasibility of using gray feature to detect road region is verified. The experimental results demonstrate that the proposed approach can detect road regions from different kinds of urban road images effectively and is suitable for the road detection.