宋怀波, 何东健, 辛湘俊. 基于机器视觉的非结构化道路检测与障碍物识别方法[J]. 农业工程学报, 2011, 27(6): 225-230.
    引用本文: 宋怀波, 何东健, 辛湘俊. 基于机器视觉的非结构化道路检测与障碍物识别方法[J]. 农业工程学报, 2011, 27(6): 225-230.
    Song Huaibo, He Dongjian, Xin Xiangjun. Unstructured road detection and obstacle recognition algorithm based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(6): 225-230.
    Citation: Song Huaibo, He Dongjian, Xin Xiangjun. Unstructured road detection and obstacle recognition algorithm based on machine vision[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(6): 225-230.

    基于机器视觉的非结构化道路检测与障碍物识别方法

    Unstructured road detection and obstacle recognition algorithm based on machine vision

    • 摘要: 为了实现非结构化道路检测与障碍物的识别,提出了一种基于最小错误率贝叶斯决策与Hough变换相结合的非结构化道路检测与障碍物识别算法。算法首先将Otsu多阈值理论引入到最小错误率贝叶斯决策中并进行图像分割,然后利用Hough变换进行道路检测、提取出纯路面区域并再次进行路面分割,最后根据分割结果进行路面障碍物定位。结果表明,该算法能够有效实现非结构化道路的检测与障碍物的识别,在光影、照度变化、水渍等不利因素影响较小的情况下,具有较好的鲁棒性。

       

      Abstract: To realize the unstructured road detection and obstacle recognition, a hybrid algorithm based on minimum error Bayesian decision theory and Hough transform was presented. The Otsu muti-threshold method was introduced into the minimum error Bayesian decision theory and the image was divided into road area and the other area. And then, Hough transform was used to detect the road and off-road borders, and the road area was re-segmented to detect the obstacles on the road. At last, the obstacles were located by outer rectangular bounds. Experimental results showed that the algorithm presented in this paper has good ability to detect the road borders and obstacles. Experiments also indicate that the method was robust when the negative influences such as shadows, changes in illumination, and water stains are slight.

       

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