基于彩色模型和近邻法聚类的水田秧苗列中心线检测方法

Detection of rice seedlings rows' centerlines based on color model and nearest neighbor clustering algorithm

水田秧苗列中心线的检测是实现水田除草机器人自主导航的重要保证。在秧苗的不同生长时期,秧苗形态各不相同;并且在南方地区的水田中经常会出现的绿色浮萍、蓝藻,它们的颜色与目标秧苗非常接近,这给秧苗的分割以及列中心线的检测带来很大的困难。针对这些问题,提出一种基于彩色模型和近邻法聚类实现秧苗列中心线的检测方法。首先,基于彩色模型即2G-R-B模型(2Green-Red-Blue)和HSI(Hue,Saturation and Intensity)彩色空间中提取S分量提取秧苗灰度特征;然后,在保持秧苗原有形状的前提下提取秧苗特征点,获得秧苗特征点图像;最后,基于近邻法利用特征点间的邻近关系对特征点进行聚类,采用基于已知点的Hough变换(known point Hough transform)提取秧苗列中心线。试验表明:提出的方法能够在图像中存有绿色浮萍、蓝藻等噪声情况下准确提取秧苗灰度特征,平均每幅真彩色图像(分辨率:1280×960)整个流程所需时间小于350ms,并能够适应自然光线变化。提出的方法能够适应环境的变化,满足机器人实时性要求。

Centerline detection of rice seedling rows in paddy fields plays an important role in autonomous navigation of weeding robots.Since the paddy field environment varies from the different growth stages of rice seedling and the color of rice seedling is similar to cyanophytes and duckweed contained in the paddy field image in South China,the segmentation of rice seedlings and centerlines detection become very challenging.This paper presents a method to detect rice seedlings rows’ centerlines based on color model and nearest neighbor clustering algorithm.The features of rice seedlings are firstly extracted based on 2G-R-B color model and S component in HSI space.The feature points of rice seedlings are then extracted without changing their shapes,and clustered based on nearest neighbor clustering algorithm according to their adjacent relationship.Finally,the centerlines can be correctly detected by applying a known point Hough transform in each cluster.The experimental results show that the feature of rice seedlings can be precisely extracted under a background with cyanophytes and duckweed,and the average processing time of a 1280 960 resolution color image is less than 350 ms.In addition,the algorithms are adapt to the environmental variation and meet the real-time requirement for agricultural robots.