油菜直播机导航路径识别方法研究
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摘要
针对油菜直播机视觉导航路径识别效果受天气、稻茬噪声等影响的难题,提出一种结合小波变换和改进随机抽样一致性(RANSAC)的导航路径识别方法。首先,对原始图像灰度化后进行小波变换,在大尺度低分辨率下凸显导航路径宏观轮廓;然后利用直播机作业区与未作业区图像对比度大的特点获取导航路径上的特征点集合;最后针对获取的特征点集合运用结合预检验和后处理校正的改进随机抽样一致性算法区分内外点,并对内点集运用最小二乘法进行导航路径直线拟合,从而获取导航路径参数。田间图像测试表明,该方法可以稳定、准确地检测出导航路径,正确率达到96.7%,同时每帧图像的处理时间在31 ms以内,能为油菜直播机的视觉导航提供技术支撑。
In practical applications,the complexity of the farmland environment including weather and rice stubble noise has resulted in bad effect of vision navigation of rapeseed direct seeder. To solve this problem,a method of identifying the vision navigation of rapeseed direct seeder based on wavelet transform and improving random sample consensus( RANSAC) was proposed. The original image grayed was wavelet transformed to highlight the macro profile of the navigation path. The feature of work area and non-work area with high-contrast in the work images of rapeseed direct seeder was used to find the feature points of navigation path through ROI sliding windows. The improved RANSAC with correction of pre-test and post-process was used to distinguish inner points and outer points in the set of navigation path feature points,and the navigation path parameters was calculated by least squares in the set of inner points. Results of field experimental tests showed that the algorithm could detect stably and accurately the navigation path with the correct rate of 96. 7%. The processing time for each frame was less than 31. 0 ms. It will provide technical support for identifying the vision navigation of rapeseed direct seeder.
In practical applications,the complexity of the farmland environment including weather and rice stubble noise has resulted in bad effect of vision navigation of rapeseed direct seeder. To solve this problem,a method of identifying the vision navigation of rapeseed direct seeder based on wavelet transform and improving random sample consensus( RANSAC) was proposed. The original image grayed was wavelet transformed to highlight the macro profile of the navigation path. The feature of work area and non-work area with high-contrast in the work images of rapeseed direct seeder was used to find the feature points of navigation path through ROI sliding windows. The improved RANSAC with correction of pre-test and post-process was used to distinguish inner points and outer points in the set of navigation path feature points,and the navigation path parameters was calculated by least squares in the set of inner points. Results of field experimental tests showed that the algorithm could detect stably and accurately the navigation path with the correct rate of 96. 7%. The processing time for each frame was less than 31. 0 ms. It will provide technical support for identifying the vision navigation of rapeseed direct seeder.
引文
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[3]孟庆宽,何洁,仇瑞承,等.基于机器视觉的自然环境下作物行识别与导航线提取[J].光学学报,2014(7):172-178.
[4]姜国权,柯杏,杜尚丰,等.基于机器视觉和随机方法的作物行提取算法[J].农业机械学报,2008,39(11):85-93.
[5]张勤,黄小刚,李彬.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[J].农业工程学报,2012,28(17):163-171.
[6]吴刚,谭彧,郑永军,等.基于改进Hough变换的收获机器人行走目标直线检测[J].农业机械学报,2010,41(2):176-179.
[7]李景彬,陈兵旗,刘阳,等.采棉机视觉导航路线图像检测方法[J].农业工程学报,2013,29(11):11-19.
[8]张方明.田间路径识别算法和基于立体视觉的车辆自动导航方法研究[D].杭州:浙江大学,2006.
[9]刘羽祚,夏俊芳.水田旋耕路径的识别方法[J].华中农业大学学报,2016,35(2):129-132.
[10]王新忠,韩旭,毛罕平,等.基于最小二乘法的温室番茄垄间视觉导航路径检测[J].农业机械学报,2012,6(6):161-166.
[11]高国琴,李明.基于K-means算法的温室移动机器人导航路径识别[J].农业工程学报,2014,7(7):25-33.
[12]陈艳,张漫,马文强,等.基于GPS和机器视觉的组合导航定位方法[J].农业工程学报,2011,27(3):126-130.
[13]陈娇,姜国权,杜尚丰,等.基于垄线平行特征的视觉导航多垄线识别[J].农业工程学报,2010,25(12):107-113.
[14]FISHER M A,BOLLES R C.Random sample consensus:a paradigm for model fitting with apphcatlons to image analysis and automated[J].Communications of the ACM,1981,24(6):381-395.
[15]林桂潮,邹湘军,罗陆锋,等.改进随机样本一致性算法的弯曲果园道路检测[J].农业工程学报,2015,31(4):168-174.
[16]彭红,肖进胜,沈三明,等.一种基于随机抽样一致性的车道线快速识别算法[J].上海交通大学学报,2014,48(12):1721-1726.
[17]高世伟,郭雷,杜亚琴,等.提升小波变换及其在图像处理中的应用[J].计算机工程与设计,2007,28(9):142-147.