Defect Detection Using Unanimous Vote Among Mahalanobis Classifiers for Each Color Component

Natsuki Sano (), Yuki Mori () and Tomomichi Suzuki ()
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Natsuki Sano: Onomichi City University
Yuki Mori: Tokyo University of Science
Tomomichi Suzuki: Tokyo University of Science

The Review of Socionetwork Strategies, 2017, vol. 11, issue 2, 173-183

Abstract: Abstract In manufacturing industries, product inspection is automated and the use of image data is increasingly being employed for defect detection. A manufacturing company in Japan produces an item and inspects the produced products using image data. Reducing the error rate is important in product inspection because poor inspection of products might lead to the delivery of defective products to consumers (consumer’s risk) and strict inspection increases production cost (producer’s risk). To reduce the error rate, we highlighted fault points using a two-dimensional moving range filter and discriminated defect production through a unanimous vote among Mahalanobis classifiers for each color component. For results, we achieved a lower error rate than the current system. This research is an empirical study of how to use image data in defect detection.

Keywords: Quality control; Defect detection; Image data; Two-dimensional moving range filter; Unanimous vote (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12626-017-0015-0

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