基于机器视觉的脐橙品质在线分级检测

Online Grade Detection of Navel Orange Quality Based on Machine Vision

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。结果表明,该分类器正确识别率可达90.5%,单个脐橙处理时间为165 ms,具有识别率高、实时性好的特点,适合于实时环境下的脐橙分级检测。

To replace artificial vision with machine vision and realize grade detection of navel orange quality, mathematical morphological was used to separate navel orange from background. The bulk features, surface defect features, colour features and texture features were extracted and used as the input feature vectors of the support vector machine, support vector machine was used in training and classification of those feature. The trained classifier was used to detect the navel orange. Results showed that the classifier had high rate of correct identification and real-time, which could be used in real-time detection of navel orange.