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Open AccessArticle

Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest

1
Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea
2
Data Science, SK Hynix Semiconductor, Icheon 17336, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(6), 932; https://doi.org/10.3390/app8060932
Received: 14 May 2018 / Revised: 31 May 2018 / Accepted: 31 May 2018 / Published: 5 June 2018
(This article belongs to the Special Issue Intelligent Imaging and Analysis)
With the coming of the 4th industrial revolution era, manufacturers produce high-tech products. As the production process is refined, inspection technologies become more important. Specifically, the inspection of a printed circuit board (PCB), which is an indispensable part of electronic products, is an essential step to improve the quality of the process and yield. Image processing techniques are utilized for inspection, but there are limitations because the backgrounds of images are different and the kinds of defects increase. In order to overcome these limitations, methods based on machine learning have been used recently. These methods can inspect without a normal image by learning fault patterns. Therefore, this paper proposes a method can detect various types of defects using machine learning. The proposed method first extracts features through speeded-up robust features (SURF), then learns the fault pattern and calculates probabilities. After that, we generate a weighted kernel density estimation (WKDE) map weighted by the probabilities to consider the density of the features. Because the probability of the WKDE map can detect an area where the defects are concentrated, it improves the performance of the inspection. To verify the proposed method, we apply the method to PCB images and confirm the performance of the method. View Full-Text
Keywords: image inspection; non-referential method; feature extraction; fault pattern learning; weighted kernel density estimation (WKDE) image inspection; non-referential method; feature extraction; fault pattern learning; weighted kernel density estimation (WKDE)
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Yuk, E.H.; Park, S.H.; Park, C.-S.; Baek, J.-G. Feature-Learning-Based Printed Circuit Board Inspection via Speeded-Up Robust Features and Random Forest. Appl. Sci. 2018, 8, 932.

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