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Defect detection of bare printed circuit boards based on gradient direction information entropy and uniform local binary patterns

Yunfeng Li (School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, China and Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, China)
Shengyang Li (Henan University of Science and Technology, Luoyang, China)

Circuit World

ISSN: 0305-6120

Article publication date: 6 November 2017

239

Abstract

Purpose

The purpose of this paper is to propose a defect detection method of bare printed circuit boards (PCB) with high accuracy.

Design/methodology/approach

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.

Findings

Experimental results show that the proposed algorithm can accurately detect typical defects of the bare PCB, such as short circuit, open circuit, scratches and voids.

Originality/value

Considering the limitations of describing all kinds of defects on bare PCB by using single kind of feature, the gradient direction information entropy and the local binary patterns were fused to build a feature vector, which evaluates quantitatively the significant degree of the defect features.

Keywords

Citation

Li, Y. and Li, S. (2017), "Defect detection of bare printed circuit boards based on gradient direction information entropy and uniform local binary patterns", Circuit World, Vol. 43 No. 4, pp. 145-151. https://doi.org/10.1108/CW-06-2017-0028

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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