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首页> 外文会议>Mathematics of Data/Image Pattern Recognition, Compression and Encryption with Applications IX >Automatic inspection of small component on loaded PCB based on SVD and SVM
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Automatic inspection of small component on loaded PCB based on SVD and SVM

机译：基于SVD和SVM的已装载PCB上的小组件自动检查

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Automatic inspection of small components on loaded Printed Circuit Board (PCB) is difficult due to the requirements of precision and high speed. In this paper, an automatic inspection method is presented based on Singular Value Decomposition (SVD) and Support Vector Machine (SVM). For the image of loaded PCB, we use prior location of component to get approximate region of the small component. Then the accurate numeral region of the small component can be segmented by using the projection data of this region. Next, Singular Values (SVs) of the numeral region can be obtained through SVD of the gray image. These SVs are used as the features of small component to train a SVM classifier. Then, the automatic inspection can be completed by using trained SVM classifier. The method based on projection data can overcome some difficulties of traditional method using connected domain, and reduce complexity of template matching. The SVD avoids using binary image to analyze the numerals, so the numeral information is retained as much as possible. Finally, the experimental results prove that the method in this paper is effective and feasible to some extent.
机译：由于对精度和高速的要求，很难自动检查已装载的印刷电路板（PCB）上的小型组件。本文提出了一种基于奇异值分解（SVD）和支持向量机（SVM）的自动检测方法。对于加载的PCB的图像，我们使用组件的先前位置来获取小组件的近似区域。然后，可以使用该区域的投影数据对小组件的精确数字区域进行分割。接下来，可以通过灰色图像的SVD获得数字区域的奇异值（SVs）。这些SV用作训练SVM分类器的小型组件的功能。然后，可以使用训练有素的SVM分类器完成自动检查。基于投影数据的方法可以克服传统的连通域方法的一些难题，并减少模板匹配的复杂度。 SVD避免使用二进制图像来分析数字，因此将尽可能保留数字信息。最后，实验结果证明了本文方法的有效性和可行性。

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