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基于双目视觉的作物苗期障碍物三维信息检测方法
引用本文:杨鹏树,刘卉,王晓翠,王侨,孟志军. 基于双目视觉的作物苗期障碍物三维信息检测方法[J]. 农机化研究, 2021, 0(4): 11-16
作者姓名:杨鹏树  刘卉  王晓翠  王侨  孟志军
作者单位:首都师范大学信息工程学院;国家农业智能装备工程技术研究中心
基金项目:国家自然科学基金项目(31571564,31571563)。
摘    要:农业智能装备在实际农田环境中行进或作业的过程中需要感知多变环境下的各种障碍物.为此,基于双目视觉,开展了作物苗期农田障碍物三维信息检测方法研究,提出了一种基于特征的障碍物检测算法.首先,利用边缘检测算法去除天空背景,提取出障碍物潜在区域的上边界线,利用超绿特征颜色变换去除绿色作物苗期农田背景,提取下边界线;然后,通过阈...

关 键 词:双目视觉  障碍物检测  边缘检测  阈值分割  立体匹配

Three-dimensional Information Detection Method for Crop Seedling Obstacles Based on Binocular Vision
Yang Pengshu,Liu Hui,Wang Xiaocui,Wang Qiao,Meng Zhijun. Three-dimensional Information Detection Method for Crop Seedling Obstacles Based on Binocular Vision[J]. Journal of Agricultural Mechanization Research, 2021, 0(4): 11-16
Authors:Yang Pengshu  Liu Hui  Wang Xiaocui  Wang Qiao  Meng Zhijun
Affiliation:(Capital Normal University Information Engineering College, Beijing 100048, China;National Engineering Research Center for Information Technology in Agriculture,Beijing 100097, China)
Abstract:Agricultural intelligent equipment needs to perceive various obstacles in a changing environment during the process of traveling or working in an actual farmland environment.Based on binocular vision,this paper studies the three-dimensional information detection algorithm of farmland obstacles in crop seedlings,and proposes a feature-based obstacle detection algorithm.Firstly,the upper boundary line of the potential area of the obstacle is extracted using the edge detection algorithm which is used to remove the sky background and the lower boundary line is extracted using the green crop color change which is used to remove the green crop seedling farmland background.Then the obstacle target area is extracted by the threshold segmentation algorithm.Finally,the disparity was obtained by stereo matching of barycenter feature points,and three-dimensional reconstruction was carried out based on internal and external camera parameters obtained by Matlab calibration,and distance,width and height of obstacle were calculated.The field experiment results show that the algorithm can correctly extract the obstacle target area,and the average relative error of obstacle distance,width and height detection are 4.7%,5.79%and 1.78%,respectively,which can meet the needs of agricultural intelligent equipment field obstacle detection.
Keywords:binocular vision  obstacle detection  edge detection  threshold segmentation  stereo matching
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