一种基于高斯核支持向量机的非结构化道路环境植被检测方法

Vegetation Detection Approach Based on Gaussian Kernel Support Vector Machine in Unstructured Road Environment

非结构化道路环境复杂多变,但是路两旁的植被较为显著,可用于限定路面的不可通行区域.在复杂的室外环境中,植被区域容易受到天气、阴影、路况等多种因素干扰而产生误检.为此本文提出了一种基于高斯核SVM(支持向量机)的植被检测方法,通过基于超像素的稀疏表示法来分析并学习样本多维色彩空间特征,进而构造分类准则有效获取植被信息,并采用栅格概率滤波来优化检测结果,提高检测精度.实验表明,该方法很好地解决了非结构化道路环境中的植被检测问题,对光照、路况等变化也具有较强的抗干扰能力,且具备较好的实时性和可靠性.在实际应用中,有效地限制了路面的不可通行区域,保障了移动智能机器人在复杂道路环境中的安全行驶区域.

Unstructured road environment is variable and unstable, but vegetation on both sides of road is more remark-able, which can be used to confine impassable area. In complex outdoor environment, vegetation area detection is vulnerable to multiple disturbance factors such as weather, shadow, road condition, and so on, resulting in detection error. Therefore a method of vegetation detection based on Gaussian kernel SVM （support vector machine） is proposed. Firstly, the sam-ple feature of multidimensional color space is analyzed and learned through the sparse representation based on superpixel. Then, classification criteria are created for effectively absorbing vegetation information. Also, grid probability filtering are used to optimize testing results and improve the detection accuracy. Experiments show that the approach excellently solves the vegetation detection problem in unstructured road environment, which is of strong anti-interference ability facing the changing lighting and road condition, and has superior real-time performance and reliability. In practical applications, im-passable regions on road are effectively restricted, ensuring the security area of the intelligent mobile robot in complicated road environment.