基于决策树支持向量机的苹果表面缺陷识别
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(西南大学工程技术学院,重庆 400716)

作者简介:

邱光应,男,西南大学在读硕士研究生。

通讯作者:

彭桂兰(1966—),女,西南大学教授,博士。E-mail:pgl602@163.com

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中央高校科研业务费课题(编号:XDJK2016A007);博士启动基金项目(编号:SWU114109);中央高校基本科研业务费双创项目(编号:XDJK2016E050)


Detection on surface defect of apples by DT-SVM method
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(College of Engineering and Technology, Southwest University, Chongqing 400716, China)

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    摘要:

    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-1 SVM)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。

    Abstract:

    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 to 1-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.

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引用本文

邱光应,彭桂兰,陶丹,等.基于决策树支持向量机的苹果表面缺陷识别[J].食品与机械,2017,33(9):131-135.
QIUGuangying, PENGGuilan, TAODan, et al. Detection on surface defect of apples by DT-SVM method[J]. Food & Machinery,2017,33(9):131-135.

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  • 在线发布日期: 2023-03-10
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