Abstract
A method of Printed Circuit Board (PCB) pin defect inspection is proposed in this paper. First, we input the pin location image. Then, we align images with Circle Hough Transform (CHT) [4]. Finally, we train cascade classifier with adaptive boosting [3] and Local Binary Pattern (LBP) [5], and detect pin defect to reduce false alarm rate and miss detection rate and thus enhance pin production yield rate.
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CITATION STYLE
Kao, H. P., Tung, T. C., Chen, H. Y., Wong, C. S., & Fuh, C. S. (2017). Pin defect inspection with X-ray images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10262 LNCS, pp. 465–473). Springer Verlag. https://doi.org/10.1007/978-3-319-59081-3_54
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