基于机器视觉和支持向量机的脐橙品质分级检测

摘要

为实现利用机器视觉代替人工视觉对脐橙进行品质分级检测,采用数字形态学方法把脐橙从背景中分离出来,并提取脐橙的体积、果面缺陷、颜色和纹理等几个主要特征。以这些特征量为支持向量机(support vector machine,SVM)的输入特征向量进行 SVM 分类器训练。最后,用该分类器进行脐橙分级检测。实验结果表明：该分类器具有正确识别率高、实时性好的特点,适合于实时环境下的脐橙分级检测。%Using machine vision to replace artificial vision and realizing navel orange quality grade detection, the mathematical morphology was employed to separate navel orange from background. The bulk features, surface defect features, color features and texture features were extracted, which were the input feature vectors of the support vector machine ( SVM ), and SVM is used in training and classification of those features. The trained classifier was used to detect navel oranges. Experimental results show that the classifier has the feature of higher rate of correct identification and real-time, and it can be used in real-time detection of navel oranges.