CAJ | 학술논문

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。
위실현이용궤기시각대체인공시각대제등진행품질분급검측,채용수자형태학방법파제등종배경중분리출래,병제취제등적체적、과면결함、안색화문리등궤개주요특정。이저사특정량위지지향량궤(support vector machine,SVM)적수입특정향량진행 SVM 분류기훈련。최후,용해분류기진행제등분급검측。실험결과표명：해분류기구유정학식별솔고、실시성호적특점,괄합우실시배경하적제등분급검측。
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.