基于中值点Hough变换玉米行检测的导航线提取方法  

Extracting navigation line to detect the maize seedling line using median-point Hough transform

作　　者：李霞[1,2,3] 苏筠皓 岳振超 王思超 周海波 Li Xia;Su Junhao;Yue Zhenchao;Wang Sichao;Zhou Haibo(Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control,Tianjin University of Technology,Tianjin 300384,China;National Demonstration Center for Experimental Mechanical and Electrical Engineering Education,Tianjin University of Technology,Tianjin 300384,China;School of Mechanical Engineering,Tianjin University of Technology,Tianjin 300384,China)

机构地区：[1]天津理工大学天津市先进机电系统设计与智能控制重点实验室,天津300384 [2]天津理工大学机电工程国家级实验教学示范中心,天津300384 [3]天津理工大学机械工程学院,天津300384

出　　处：《农业工程学报》2022年第5期167-174,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然基金项目(32060417,32171902)。

摘　　要：为解决机器视觉对早期玉米苗带在多环境变量下导航线提取耗时长、准确率低的问题,该研究提出了一种基于中值点Hough变换作物行检测的导航线提取算法。首先,改进了传统的2G-R-B算法,再结合中值滤波、最大类间方差法和形态学操作实现土壤背景与玉米苗带的分割。其次,通过均值法提取玉米苗带特征点,然后采用中值点Hough变换拟合垄间两侧玉米苗列线,最后将检测出的双侧玉米苗列线为导航基准线,利用夹角正切公式提取导航线。试验结果表明:改进的灰度化算法能够正确分割玉米苗带与土壤,处理一幅640×480像素彩色图像平均耗时小于160 ms,基于中值点Hough变换检测玉米苗列再提取导航线的最大误差为0.53°,相比于传统Hough变换时间上平均快62.9 ms,比最小二乘法平均精确度提高了7.12°,在农田早期玉米苗带多环境变量影响因素下导航线提取准确率均达92%以上,具有较强的可靠性和准确性。Automatic navigation has been crucial to realizing robot automation in agricultural robots. Most current extraction approaches to navigation paths were clumsy and time-consuming susceptible to interference. It is a high demand for the real-time, high accuracy, and robustness of the navigation line extraction in plant protection robots. Taking the early maize seedlings as the research object, a navigation line extraction of a plant protection robot was realized to detect the crop line using the median-point Hough transform. The angle traversal range of the intersection point was reduced than before. Only characteristic point curves were calculated for the walking control of the robot, such as the intersection point of the median point curve. Five steps were contained in the navigation line extraction of the plant protection robot: image acquisition, image segmentation, crop line feature point extraction, crop line fitting, and navigation line extraction. Firstly, the RGB images were preprocessed to highlight the green crops in the soil background, where the color RGB images were grayed to improve the gray factor. Then, the Otsu method was selected for the adaptive threshold to search for the crop rows. The image binarization was segmented into the soil background and crop. As such, the local noise was filtered to clearly segment the crop row images,according to multiple morphological operations. A vertical projection was also utilized to divide the region of interest of the crop row along the abscissa of the pixel coordinate system. Secondly, the feature points were extracted for the line fitting of the crop row. The median value point Hough transform was then used to fit the crop line between the two sides of the ridges.Finally, the angle tangent formula was used to extract the path navigation line of the plant protection robot, where the detected crop line between the two sides of the ridges was taken as the reference. The results showed that the improved gray factor clearly separated the crops and soil, accordi

关 键 词：机器视觉 特征点提取 作物行检测 导航线提取 HOUGH变换 导航 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]