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"Defect Detection of Bare Printed Circuit Boards Based on Gradient Direction Information Entropy and Uniform Local Binary Patterns" Authors: Yunfeng Li and Shengyang Li

Abstract: This paper proposes a defect detection method of bare PCBs with high accuracy. First, bilateral filtering of the PCB image was performed in the uniform color space, and the copper-clad areas were segmented according to the color difference among different areas. Then, according to the chaotic characteristics of the spatial distribution and the gradient direction of the edge pixels on the boundary of the defective areas, the feature vector, which evaluates quantitatively the significant degree of the defect characteristics by using the gradient direction information entropy and the uniform local binary patterns, was constructed. Finally, support vector machine classifier was used for the identification and localization of the PCB defects. Experimental results show the proposed algorithm can accurately detect typical defects of the bare PCB, such as short circuit, open circuit, scratches and voids. (Circuit World, vol. 4, no. 4,

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Title Annotation:TECHNICAL ABSTRACTS: In Case You Missed It
Publication:Printed Circuit Design & Fab Circuits Assembly
Date:Mar 1, 2018
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