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

Abstract

Featured Application: The main contribution of this work is to propose an inspection method using image data generated at the actual manufacturing process. This proposed method can help printed circuit board (PCB) manufacturers more effectively detect defects, such as scratches and improper etching, in an automated optical inspection (AOI). Moreover, the proposed method of this work can be also applied to the field of dermatology, where it has to detect skin diseases, as well as in PCB inspection.Abstract: 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.

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