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首页> 外文会议>International conference on machine vision >Wire Connector Classification with Machine Vision and a Novel Hybrid SVM
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Wire Connector Classification with Machine Vision and a Novel Hybrid SVM

机译：具有机器视觉和新型混合SVM的电线连接器分类

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A machine vision-based system has been developed and tested that uses a novel hybrid Support Vector Machine (SVM) in a part inspection application with clear plastic wire connectors. The application required the system to differentiate between 4 different known styles of connectors plus one unknown style, for a total of 5 classes. The requirement to handle an unknown class is what necessitated the hybrid approach. The system was trained with the 4 known classes and tested with 5 classes (the 4 known plus the 1 unknown). The hybrid classification approach used two layers of SVMs: one layer was semi-supervised and the other layer was supervised. The semi-supervised SVM was a special case of unsupervised machine learning that classified test images as one of the 4 known classes (to accept) or as the unknown class (to reject). The supervised SVM classified test images as one of the 4 known classes and consequently would give false positives (FPs). Two methods were tested. The difference between the methods was that the order of the layers was switched. The method with the semi-supervised layer first gave an accuracy of 80% with 20% FPs. The method with the supervised layer first gave an accuracy of 98% with 0% FPs. Further work is being conducted to see if the hybrid approach works with other applications that have an unknown class requirement.
机译：已开发并测试了基于机器视觉的系统，该系统在具有透明塑料线连接器的零件检查应用中使用了新型混合支持向量机（SVM）。该应用程序要求系统区分4种不同的已知样式的连接器和一种未知的样式，总共5类。处理未知类的要求是使用混合方法的必要条件。该系统接受了4个已知类别的培训，并进行了5个类别的测试（4个已知类别加上1个未知类别）。混合分类方法使用了两层SVM：一层是半监督的，另一层是监督的。半监督SVM是无监督机器学习的特殊情况，它将测试图像分类为4个已知类（接受）或未知类（拒绝）中的一个。监督的SVM将测试图像分类为4个已知类别之一，因此会给出假阳性（FP）。测试了两种方法。这些方法之间的差异在于切换了层的顺序。使用半监督层的方法首先使用20％的FP给出了80％的精度。带有监督层的方法首先使用0％FP给出了98％的精度。正在进行进一步的工作，以查看混合方法是否可与其他具有未知类别要求的应用程序一起使用。

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