基于机器视觉的温室番茄裂果检测  

Detection of cracking tomato based on machine vision in greenhouse

作　　者：刘鸿飞[1] 黄敏敏 赵旭东 陆文婷[3] Liu Hongfei;Huang Minmin;Zhao Xudong;Lu Wenting(School of Mechanical Engineering,University of Science and Technology Beijing,Beijing 100083,China;National Agricultural Science and Technology Exhibition Park of Chinese Academy of Agricultural Sciences,Beijing 100081,China;School of Business Administration,Capital University of Economics and Business,Beijing 100070,China)

机构地区：[1]北京科技大学机械工程学院,北京100083 [2]农科院国家农业科技展示园,北京100081 [3]首都经济贸易大学工商管理学院,北京100070

出　　处：《农业工程学报》2018年第16期170-176,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(71401111);北京社会科学基金项目(No.15JGB212)

摘　　要：该文通过对温室番茄果实进行定位及裂果检测,可为番茄裂果率预估及后续裂果自动筛选提供参考。针对自然光照下采集的各类番茄图像,在相关颜色空间中进行阈值预分割,利用前期支持向量机训练得到的纹理特征分类器对预分割区域进行二次判别;之后在前景区域利用显著性角点分割构造边缘轮廓集,利用基于最小二乘法修正的改进霍夫变换拟合单个番茄目标;最后利用二维Gabor小波算子对拟合的单个番茄区域进行纹理特征提取及裂果判别。文中共采集82幅番茄图像,其中50幅图像作为训练集图像,32幅图像作为验证集,所提算法对测试集中总共128个番茄的果实正确检出率为91.41%,对其中35裂果的正确判别率为97.14%,裂果判别部分平均耗时21 ms。试验结果表明,该方法具有较好的鲁棒性与可靠性,对成熟期番茄裂果率的估计研究及采摘过程中裂果的自动分级筛选具有较好的指导意义,为未来实现温室番茄果实生长状态在线监测提供参考。A new combined algorithm is put forward to facilitate the prediction of tomato cracking rate and automatic screening of dehiscent fruit.In order to improve the recognition accuracy and reduce the segmentation error in natural illumination,different color spaces of the original image were compared in the preliminary segmentation section,then multi-channel in color space that including R-Bchromatic aberration characteristic,normalized R channel and Hue channel were chosen.For the pre-segmentation may include some non-target areas,the relevant texture features were used to make a secondary identification of potential areas.In this study,SVM(support vector machine)was built based on fruit areas and non-fruit areas of a certain size(10×10 pixels)extracted from the training image.5 texture features,including standard deviation,smoothness,third-moment,energy,and entropy were calculated for those fruit areas and non-fruit areas,thus the regions of target and background could be successfully separated by the algorithm.Then,the edges and contours,extracted in this foreground area,were used to construct the contour dataset.The Shi-Tomasi corner detection algorithm was implemented to split the contours in this dataset.Since the edges of the tomato fruit were mainly arc fragments,the contour set was preliminary selected according to the contour length and contour curvature.This part was especially important to simplify the contour set and improve the efficiency of subsequent calculation.Circular Hough transform(CHT)was then applied to fit the contour set.The maximum value of distance transform in foreground binary region was taken as the limit of fitting ellipse radius.If the circle radius was bigger than the maximum fruit radius,the circle would be rejected.If the distance between 2 circles was smaller than two-thirds of the maximum value,the circle would be rejected due to the heavy occlusion between 2 tomato fruits.The least square contour correction was made based on the roundness and the number of background pixels cont

关 键 词：图像处理 纹理 特征 裂果检测 霍夫变换 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]