基于机器视觉的田间水稻苗列识别算法的研究

Research on rice seedling recognition algorithm based on machine vision in rice field

利用YUV色彩空间模型,以完全查表法对水稻秧苗列图像进行灰度化,通过基于傅里叶变换指导生成图像形态学运算的结构元素,提出一种结合傅里叶变换进行膨胀和腐蚀的方法,提取秧苗列轮廓,采用改良的逆投影变换对苗列图像进行垂直俯视投影,得到实际田间苗列位置,进而利用苗列实际走向信息,实现机器视觉导航系统跟踪苗列行进。对摄像机不同角度获取的苗列图像的处理结果表明,在容许识别定位误差小于50像素点、角度偏差小于6°的前提下,对苗列中心线识别与提取导航基准线的准确率为95.2%,可较好地实现田间自然环境下秧苗图像背景分割和苗列中心线提取。

The YUV color space model is used to grayscale rice seedling images with complete table searching method, which removes the influence of illumination intensity and improves the real–time performance of the system. The structural elements of the morphological operation of the images are generated by the guidance of Fourier transform. A method of expansion and corrosion combined with Fourier transform is proposed to extract the contour of each seedling line. The vertical overlooking projection of the seedling sequence image is carried out by the modified inverse projection transform to obtain the actual seedling row position in the field. Then the machine vision navigation system is used to track the train. The results of image processing in different angles of the image show that the accuracy of guiding datum line recognition and extraction of seedling centerline is 95.2% when the recognition error is less than 50 pixels and the angle deviation is less than 6°. It is found that the background segmentation of seedling image and the extraction of centerline of seedling train are well realized under field natural environment.