机器视觉技术在荔枝识别与定位研究中的应用  

Study on recognition and positioning of litchi based on technology of machine vision

作　　者：郭艾侠[1] 彭明明[1] 邢仲璟[1] GUO Aixia;PENG Mingming;XING Zhongjing(College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China)

机构地区：[1]华南农业大学数学与信息学院,广州510642

出　　处：《计算机工程与应用》2017年第17期218-223,259,共7页Computer Engineering and Applications

基　　金：广东省自然科学基金博士启动资助项目(No.2014A030310275;No.2015A030310258);广东省科技计划资助项目(No.2015A020224034);国家星火计划资助项目(No.2014GA780057)

摘　　要：为解决串型荔枝图像识别和定位问题,提出以Harris与SIFT算法融合的采摘点计算与匹配为基础,进行平行双目立体视觉模型下采摘点定位的研究方案。首先对荔枝YCb Cr色彩空间的Cr灰度图进行二次阈值分割,分类识别出荔枝串、荔枝果与结果母枝。其次,提取识别果实区域的最小外接矩形、质心等特征信息,结合在结果母枝上检测的Harris特征点计算出采摘点的二维图像坐标,并对计算采摘点进行基于SIFT向量搜索的立体匹配。最后,对计算采摘点进行视觉定位及其深度误差分析实验,实验数据表明:在354~590 mm距离范围内,插值补偿后的采摘点的定位深度误差小于10 mm,能够较好满足荔枝采摘机器人的现有技术要求。To resolve the problem on fast recognition and accurate location faced by the robot of picking litchi,a scheme on location of picking point based on parallel binocular stereo vision model is proposed,which is supported by the computation and matching of picking point obtained from combined algorithm of Harris and SIFT.Firstly,litchi cluster,litchi fruit and its main fruit bearing branch are sortedly recognized,by segmenting Cr gray picture of YCbCr with twin-threshold method.Secondly,region feature information on centroid and Minimum Bounding Rectangle(MBR)of litchi is extracted,along with corners from the recognized main fruit bearing branch detected by Harris algorithm,and two-dimensional coordinate pixel on the picking point is achieved.After stereo matching of the picking point based on SIFT vector search is accomplished,the experiment on picking location and its error analysis is finally carried out.The experimental data shows that depth error on location of the picking point commenced by interpolation method is less than10mm,under shooting distance of354~590mm,which is better satisfaction for litchi-picking robot.

关 键 词：机器视觉 立体匹配 图像识别 荔枝采摘机器人 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]