视觉机器人对于快速道路检测问题的研究

Research on Fast Road Detection Problem for Visual Robots

针对复杂环境下视觉机器人道路检测算法的抗干扰性差、速度慢的缺点,为抑制噪声的干扰,提高算法的运行速度,提出了一种基于边缘和区域相结合的道路检测算法。算法先采用Canny算子求出图像的边缘,再根据道路的色彩信息进行自适应的区域分割,然后结合图像边缘信息和区域分割信息确定出道路的边界区域,最后对该区域的边缘图像进行Hough变换检测道路。实验结果表明,边缘提取的检测算法有效地提高了算法的抗噪性能和运行速度,具有更好的道路检测效果。

Common road detection algorithm for visual robots in complex environment has the shortcomings of bad anti-interference and poor speed. In order to suppress noise disturbance and improve the execution speed of the algo- rithm, a road detection algorithm based on the combination of edge and region was proposed. The algorithm firstly u- ses the Canny operator to find the image edge, and performs the adaptive region segmentation according to the color information of the road, then combines edge information with region segmentation information to identifies the road boundary region. Finally, it performs Hough transform based on edge image of the region to detect the road. The ex- perimental results show that compared with the traditional road detection algorithm, this algorithm can effectively im- prove the ability of anti-noise and running speed with a better road detection results.