湖北农业科学
湖北農業科學
호북농업과학
2014年
9期
2160-2164
,共5页
机器视觉%支持向量机%品质分级%脐橙
機器視覺%支持嚮量機%品質分級%臍橙
궤기시각%지지향량궤%품질분급%제등
machine vision%support vector machine%quality grade%navel orange
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。
為實現利用機器視覺代替人工視覺對臍橙進行品質分級檢測,採用數字形態學方法把臍橙從揹景中分離齣來,併提取臍橙的體積、果麵缺陷、顏色和紋理等幾箇主要特徵；以這些特徵量為支持嚮量機的輸入特徵嚮量進行SVM分類器訓練；最後用該分類器進行臍橙分級檢測。結果錶明,該分類器正確識彆率可達90.5%,單箇臍橙處理時間為165 ms,具有識彆率高、實時性好的特點,適閤于實時環境下的臍橙分級檢測。
위실현이용궤기시각대체인공시각대제등진행품질분급검측,채용수자형태학방법파제등종배경중분리출래,병제취제등적체적、과면결함、안색화문리등궤개주요특정；이저사특정량위지지향량궤적수입특정향량진행SVM분류기훈련；최후용해분류기진행제등분급검측。결과표명,해분류기정학식별솔가체90.5%,단개제등처리시간위165 ms,구유식별솔고、실시성호적특점,괄합우실시배경하적제등분급검측。
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 ma-chine was used in training and classification of those feature. The trained classifier was used to detect the navel orange. Re-sults showed that the classifier had high rate of correct identification and real-time , which could be used in real-time detec-tion of navel orange.
