机器人2015，Vol.37Issue(6)：702-707,6.DOI:10.13973/j.cnki.robot.2015.0702
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
摘要
Abstract
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.关键词
非结构化道路/高斯核支持向量机(SVM)/超像素/栅格概率滤波/植被检测Key words
unstructured road/Gaussian kernel support vector machine (SVM)/superpixel/grid probability filter/vegeta-tion detection分类
信息技术与安全科学引用本文复制引用
周植宇,杨明,薛林继,王春香,王冰..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[J].机器人,2015,37(6):702-707,6.基金项目
国家自然基金重大研究计划资助项目(91420101) （91420101）
国家自然科学基金资助项目(61174178,51178268). （61174178,51178268）
