宋宇,刘永博,刘路,朱德泉,焦俊,陈黎卿.基于机器视觉的玉米根茎导航基准线提取方法[J].农业机械学报,2017,48(2):38-44.
SONG Yu,LIU Yongbo,LIU Lu,ZHU Dequan,JIAO Jun,CHEN Liqing.Extraction Method of Navigation Baseline of Corn Roots Based on Machine Vision[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(2):38-44.
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基于机器视觉的玉米根茎导航基准线提取方法   [下载全文]
Extraction Method of Navigation Baseline of Corn Roots Based on Machine Vision   [Download Pdf][in English]
投稿时间:2016-03-15  修订日期:2017-02-10
DOI:10.6041/j.issn.1000-1298.2017.02.005
中文关键词:  农业自动导引车  机器视觉  导航基准线  峰值点检测  玉米根茎
基金项目:国家自然科学基金项目(31671589)、农业部公益性行业专项(201503136-2)、安徽省科技攻关计划项目(1501031104)、安徽省教育厅自然科学研究重点项目(KJ2013A107)、安徽农业大学稳定和引进人才基金项目(WD2013-11)和安徽农业大学学科骨干培育项目(2014XKPY-49)
作者单位
宋宇 安徽农业大学工学院 
刘永博 安徽农业大学工学院 
刘路 中国科学技术大学工程科学学院 
朱德泉 安徽农业大学工学院 
焦俊 安徽农业大学工学院 
陈黎卿 安徽农业大学工学院 
中文摘要:提出一种在大田环境下快速、精确提取中晚期玉米行中心线作为农业机器人导航基准线的新方法。改进了传统的2G-R-B算法,实时地获取植株绿色特征。根据玉米垂直投影图生成根茎轮廓特点并采用峰值点检测算法生成玉米根茎候补定位点,再对候补定位点进行二次判别,提取玉米根茎定位点。利用最小二乘法对已知特征点进行拟合,得到作物行线。求取左右行斜率后,计算出实际需要的导航基准线。实验结果表明,与其它算法相比,处理一幅700像素×350像素的彩色图像平均耗时小于185ms,实时性好。在多种环境下生成的导航基准线准确率在90%以上,有较强的鲁棒性,为农业自动导引车(Automated guided vehicle,AGV)在中后期玉米大田中的自主行走提供了一种可靠的导航方法。
SONG Yu  LIU Yongbo  LIU Lu  ZHU Dequan  JIAO Jun  CHEN Liqing
School of Engineering, Anhui Agricultural University,School of Engineering, Anhui Agricultural University,School of Engineering Science, University of Science and Technology of China,School of Engineering, Anhui Agricultural University,School of Engineering, Anhui Agricultural University and School of Engineering, Anhui Agricultural University
Key Words:agricultural AGV  machine vision  navigation baseline  detection of peak points  corn root
Abstract:In order to achieve small agricultural automated guided vehicle (AGV) which could navigate autonomously between corn rows, a method was proposed which could quickly and accurately extract the centerlines of middle late corn rows as the innovative methods of navigation baseline of agricultural robot in field environment. The algorithm was improved by the traditional 2G-R-B algorithm so that it could obtain the characteristics of green plants in real-time and also improve the robustness of image pre-processing. According to the vertical projection of corn crop’s line, points of profile features of corn roots were generated. Using the detection algorithm of peak points, the backup location points of corn roots were obtained and then the location points of corn roots were got after the second judgment and detection. The least square method was used for fitting the location points of corn roots and two lines of crop rows were generated. The actual navigation baseline was calculated based on the formula angle bisector after the line slopes of two crop rows were generated respectively. In addition, the camera calibration process was simplified so that the image pixel coordinates could be converted into world coordinates quickly. The extracted angle and lateral deviation of navigation baselines were used as input parameters of navigation to control agriculture AGV. The experimental results showed that the method had strong robustness which could adapt to different environments and the accuracy of navigation baselines by detecting was more than 90%. The average processing time of a 700 pixels × 350 pixels color image was less than 185 ms which had a better real-time. The results provided a reliable reference method for autonomous navigation of the agricultural AGV in middle-late corn field.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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