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基于机器视觉的苹果品质在线分级检测

Journal of Agricultural Science and Technology(2018)

Cited 14|Views64
Abstract
目前苹果分级自动化程度较低,为了实现苹果品质自动、快速、准确分级设计了一套苹果智能在线检测分级系统.以寒富苹果为测试对象,采用机器视觉技术对苹果分级进行研究.采用阈值分割的方法分割苹果正面图像,逐像素遍历法提取苹果外部轮廓,通过计算其各点到重心的距离提取苹果大小特征,同时计算苹果横径与纵径比提取果形特征.采用支持向量机方法分割侧面苹果图像,计算苹果红色像素占苹果像素的比例提取颜色特征,利用Fisher统计识别的方法提取苹果缺陷.实现了整个分级系统的硬件搭建以及软件的功能,利用该系统对400个苹果样本进行了分级试验,结果表明该系统分级的苹果总体正确率达到95%.设计的基于机器视觉的苹果智能在线检测分级系统克服了传统分级方法的不足,加快了苹果品质分级自动化速度,对水果品质分级等领域有重要研究意义.
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machine vision,apple grading,pixel-by-pixel traversal method,SVM,feature extraction,on-line detection
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要点】:本文设计了一套基于机器视觉的苹果智能在线检测分级系统,实现了苹果质量的自动、快速、准确分级,提高了自动化分级水平,分级总准确率达到95%。

方法】:采用阈值分割法对苹果正面图像进行分割,通过逐像素遍历法提取苹果外轮廓,计算各点至重心距离提取苹果大小特征,以及通过计算苹果横纵径比值提取果实形状特征。使用支持向量机(SVM)方法进行苹果两侧分离,通过计算红色像素占整个苹果像素的比例提取苹果颜色特征,使用Fisher统计法提取缺陷部分。

实验】:以寒富苹果为测试对象,使用该系统对400个苹果样本进行分级,实验结果显示系统总分级准确率为95%。