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论文类型：会议论文

发表时间：2006-08-15

收录刊物：EI、CPCI-S、Scopus

卷号：6315

关键字：printed circuit board; singular value decomposition; support vector machine

摘要：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.