基于决策树支持向量机的苹果表面缺陷识别

Detection on surface defect of apples by DT-SVM method

在基于机器视觉苹果缺陷识别过程中,因果梗/花萼与缺陷表皮颜色相似,极大地降低苹果表面缺陷识别准确率,提出一种基于决策树支持向量机(DT-SVM)的苹果表面缺陷识别方法。该方法首先采用单阈值法去除背景,其次在R通道中利用Otsu法和连通域标记法提取目标区域(果梗、花萼和缺陷)的颜色、纹理和形状特征,最后利用决策树支持向量机进行识别。以600幅富士苹果图像为例,使用该方法进行缺陷识别,结果表明该方法的平均准确率为97.7%。与1-V-1多分类支持向量机(1-V-1SVM)和AdaBoost分类算法相比,DT-SVM方法正确率高、耗时短。说明决策树支持向量机对苹果表面缺陷识别十分有效。

During the process of apple blemish detection based on machine vision technology,due to the color similarity between stem/calyx and blemish,which greatly decreases the accuracy in apple detection,a method was proposed based on Decision Tree-Support vector machine（DT-SVM）to solve the challenge problem.Firstly,the single threshold method is used to remove the background.Then in the R channel,Connected Component Labeling method and Otsu method were employed to extract object regions（stem,calyx,blemish）,which were used to compute the color,texture and shape features.In the end,adopted the DT-SVM method to distinguish blemishes from the stem and calyx of apple images.By conducted on 600 apple images,the average accuracy of experiments was 97.7%.Compared to1-V-1 SVM method and AdaBoost method,the DT-SVM method had a higher accuracy and less time-consuming,which could actually validate the effectiveness of the proposed method in recognizing the blemish of the apples.