农业机器人视觉导航路径识别方法研究

详细信息    查看全文 | 推荐本文 |

英文篇名：Research on Path Recognition Method of Agricultural Robot Vision Navigation 作者：李军锋 ; 李逃昌 ; 彭继慎 英文作者：LI Junfeng;LI Taochang;PENG Jishen;College of Electrical and Control Engineering,Liaoning Technical University; 关键词：农业机器人 ; 机器视觉 ; 路径规划 ; 偏移行中线 ; 混合阈值法 英文关键词：agricultural robot;;machine vision;;path planning;;offset line midline;;mixed threshold method 中文刊名：JSJC 英文刊名：Computer Engineering 机构：辽宁工程技术大学电气与控制工程学院; 出版日期：2017-12-14 17:27 出版单位：计算机工程 年：2018 期：v.44;No.492 基金：辽宁省博士科研启动基金(201601364);; 辽宁工程技术大学校基金(20160050T) 语种：中文; 页：JSJC201809007 页数：8 CN：09 ISSN：31-1289/TP 分类号：44-50+64

摘要

针对农业机器人视觉路径识别的识别率低、速度慢以及易受光照、杂草影响的问题,提出基于混合阈值与偏移行中线的视觉导航路径识别方法。采用光照无关量进行光照干扰的剔除,利用混合阈值方法分割图像,给出基于偏移行中线的方法划分作物行。实验结果表明,与传统农业机器人作物行识别方法相比,该方法数据运算量小,平均耗时200 ms左右,作物行识别准确率达98%,能够有效地确定农业机器人的导航路径。
        Aiming at the problems of low recognition rate,slow speed,easy to be affected by illumination and weeds effect in the visual path recognition of agricultural robots,a visual navigation path recognition method based on mixed threshold and offset line midline is proposed. The illumination independence is used to eliminate the illumination interference. The mixed threshold method is used to segment the image,and the crop line based on the offset line midline method is given. Experimental results show that compared with the traditional agricultural robot crop line identification method,the method has a small amount of data calculation,the average time is about 200 ms,and the crop line identification accuracy rate is 98%,which can effectively determine the navigation path of the agricultural robot.

引文

[1]李盛辉,田光兆,姬长英,等.自主导航农业车辆的全景视觉多运动目标识别跟踪[J].农业机械学报,2015,46(1):1-7.
    [2]WEN X S.Development and application status of robot technology in agriculture[J].Journal of Anhui Agricultural Sciences,2007,35(11):3445-3446.
    [3]陈雪娇.农业机械导航技术发展分析[J].农业机械学报,2014,45(9):102-103.
    [4]鱼兆伟,吴晓波,沈林.基于动态感兴趣区域的光照无关车道线检测算法[J].计算机工程,2017,43(2):43-47.
    [5]张志斌,罗锡文,臧英,等.基于颜色特征的绿色作物图像分割算法[J].农业工程学报,2011,27(7):183-189.
    [6]陈俊君,徐冰.雾霾天气条件下的机器视觉图像清晰化研究[J].计算机工程,2017,43(2):280-285.
    [7]冷建伟,沈芳婷.基于HSV色彩模型与区域生长法的水文图像分割[J].计算机工程,2017,43(7):223-228.
    [8]HONG H,YANG X,YOU Z,et al.Visual quality detection of aquatic products using machine vision[J].Aquacultural Engineering,2014,63:62-71.
    [9]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,2014,34(7):172-178.
    [10]BAKER L,MILLS S,LANGLOTZ T,et al.Power line detection using Hough transform and line tracing techniques[C]//Proceedings of International Conference on Image and Vision Computing.Wellington,New Zealand:IEEE Press,2017:1-6.
    [11]杨陶,田怀文,刘晓敏,等.基于边缘检测与OTSU的图像分割算法研究[J].计算机工程,2016,42(11):255-260.
    [12]GOLZARIAN M R,LEE M K,DESBIOLLES J M A.Evaluation of color indices for improved segmentation of plant images[J].Transactions of the Asabe,2012,55(1):261-273.
    [13]陈树越,李建森,黄萍,等.基于采样约束的随机Hough变换圆检测算法[J].计算机工程,2017,43(10):259-263.
    [14]邹香玲.智能视频监控系统中的人脸识别技术之研究[J].电子技术与软件工程,2017,1(3):91-92.
    [15]扈立超,史再峰,庞科,等.用于图像匹配的改进Harris特征点检测算法[J].计算机工程,2015,41(10):216-220.