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http://dx.doi.org/10.14372/IEMEK.2021.16.6.277

Hierarchical Correlation-based Anomaly Detection for Vision-based Mask Filter Inspection in Mask Production Lines  

Oh, Gunhee (Kumoh National Institute of Technology)
Lee, Hyojin (Korea Electronics Technology Institute)
Lee, Heoncheol (Kumoh National Institute of Technology)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.6, 2021 , pp. 277-283 More about this Journal
Abstract
This paper addresses the problem of vision-based mask filter inspection for mask production systems. Machine learning-based approaches can be considered to solve the problem, but they may not be applicable to mask filter inspection if normal and anomaly mask filter data are not sufficient. In such cases, handcrafted image processing methods have to be considered to solve the problem. In this paper, we propose a hierarchical correlation-based approach that combines handcrafted image processing methods to detect anomaly mask filters. The proposed approach combines image rotation, cropping and resizing, edge detection of mask filter parts, average blurring, and correlation-based decision. The proposed approach was tested and analyzed with real mask filters. The results showed that the proposed approach was able to successfully detect anomalies in mask filters.
Keywords
Mask Production Line; Vision-based Mask Filter Inspection; Anomaly Detection; Cross-Correlation;
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