中文摘要：

针对基于机器视觉的自动导航系统现有导航线提取算法易受外界环境干扰和处理速度较慢等问题,该文提出一种基于图像扫描滤波的导航线提取方法.首先获取不同农作物的彩色图像,使用2G-R-B 算法对彩色图片进行灰度化处理,得到作物行和土壤背景对比性良好的图片.使用 Otsu 方法对图像进行分割,得到二值化的图像后,再采用腐蚀-中值滤波-膨胀的滤波方法对图像进行去噪处理.然后使用该文提出的扫描滤波导航线提取算法,将图像分成左右两部分,使用等面积三角形对两部分分别进行扫描后,再对扫描的结果进行滤波,从而提取作物行,得到导航线.试验结果表明,采用该方法处理一幅640×320像素的图像只需要76 ms,可满足农机具实时导航的要求;与传统导航线提取算法相比,该算法计算速度快,适应能力强.

英文摘要：

To make up the shortages of the existing algorithms for the visual navigation such as the noise interference and the processing speed, a new algorithm for navigation line detection was designed in this article. In the first stage, the image preprocessing was carried out. Firstly, the 2G-R-B method was used to convert the color images into grey scale images in order to distinguish crop and soil better. In general, the green component G is far greater than red R and blue B component for the crops of which main pigment is chlorophyll. The 2G-R-B method was used to graying images in order to emphasize green component and restrain the rest two components. Secondly, the OTSU method was used to transfer the grey scale images into binary images. Because the brightness and color will be different if the image under different light condition, the OTSU method was taken to transform the gray images into binary images. In addition, the weeds, shadow is inevitable because of the complexity of the farmland environment. Therefore, there are a lot of noise points in the image after binarization. In order to get ideal effect, the noise in the image needs to be reduced. In this paper, the corrosion-median filter-expansion was selected to reduce the noise according to the characteristics of the weeds. In the next stage, the designed algorithm named Scan Filtering （SF） navigation line extracting algorithm was used to get the navigation line from the above processed binary image. In SF algorithm, the image was divided into the right sector and the left sector. These two sectors were scanned by a series of triangles with the same area; then, the number of white dots was counted in every triangle and stored in an array. The array was filtered by an IIR filter, and the navigation line was extracted according to the output of the IIR filter. Finally, the parameter of navigation was transformed to the horizontal deviation and the heading deviation by the relative position and orientation algorithm. Three experiments were designed to ev