Defect Detection Using Unanimous Vote Among Mahalanobis Classifiers for Each Color Component
| Autor: | Yuki Mori, Natsuki Sano, Tomomichi Suzuki |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mahalanobis distance
business.industry Computer science education 020208 electrical & electronic engineering Word error rate Pattern recognition 02 engineering and technology Filter (signal processing) humanities stomatognathic diseases Empirical research Manufacturing Component (UML) 0202 electrical engineering electronic engineering information engineering Range (statistics) Production (economics) Artificial intelligence business |
| Zdroj: | The Review of Socionetwork Strategies. 11:173-183 |
| ISSN: | 1867-3236 |
| DOI: | 10.1007/s12626-017-0015-0 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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