马铃薯缺陷透射和反射机器视觉检测方法分析

针对反射机器视觉技术若同时检测马铃薯内外部缺陷存在检测精度不高的问题,提出一种基于透射机器视觉技术的马铃薯内外部缺陷无损检测方法。通过对获取的马铃薯透射和反射图像预处理方法的比较研究,确定上山法结合区域生长法为马铃薯透射和反射图像特征的最优分割方法;采用偏最小二乘-支持向量机分别建立了透射和反射图像的马铃薯缺陷识别模型并进行了比较。在对马铃薯内部缺陷进行检测时,透射和反射图像所建模型的判别正确率分别为96.30%、59.26%;在对马铃薯外部缺陷进行检测时,透射和反射图像所建模型的判别正确率分别为94.20%、89.86%;在对马铃薯内外部缺陷进行同时检测时,透射和反射图像所建模型的判别正确率分别为95.83%、81.25%。研究结果表明,无论是对马铃薯内部或外部缺陷单独进行检测,还是对内外部缺陷同时进行检测,透射方法均比反射方法精度更高。

Comparison of Transmission and Reflection Imaging Technologies to Detect Potato Defects Based on Machine Vision Technology

With the aim to solve the accurate rate short of reflection imaging technology to simultaneously detecting internal and external defects of potatoes, a nondestructive test technology based on transmission imaging and machine vision technology was proposed. It is concluded that the combination of hill climbing method and region growing method is the optimal image segmentation method for transmission and reflection images of potato by studying image preprocessing methods. Partial least squares-support vector machine (PLS-SVM) method was employed to establish the potato defects recognition model for transmission and reflection images of potato. In the potato internal defects detection, the classifying correct rates of the transmission and the reflection imaging technology are 96.30% and 59.26% respectively; in the potato external defects detection, the classifying correct rates are 94.20% and 89.86% respectively; in the simultaneous potato internal and external defects detection, the classifying correct rates are 95.83% and 81.25% respectively. The research results show that the transmission method is better than the reflection method in detecting potato internal and external defects alone, or in detecting the internal and external defects simultaneously.