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Effective degrees of freedom for surface finish defect detection and classification
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Volvo Group Trucks Operations.
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2019 (English)Manuscript (preprint) (Other academic)
Abstract [en]

One of the primary concerns of product quality control in the automotive industry is anautomated detection of defects of small sizes on specular car body surfaces. A new statistical learning approach is presented for surface finish defect detection based on spline smoothing method for feature extraction and k-nearest neighbour probabilistic classifier. Since the surfaces are specular, structured lightning reflection technique is applied for image acquisition. Reduced rank cubic regression splines are used to smooth the pixel values while the effective degrees of freedom of the obtained smooths serve as components of the feature vector. A key advantage of the approach is that it allows reaching near zero misclassification error ratewhen applying standard learning classifiers. We also propose probability based performance evaluation metrics as alternatives to the conventional metrics. The usage of those provides the means for uncertainty estimation of the predictive performance of a classifier. Experimental classification results on the images obtained from the pilot system located at Volvo GTO Cab plant in Umeå, Sweden, show that the proposed approach is much more efficient than the compared methods.

Place, publisher, year, edition, pages
2019. , p. 17
Keywords [en]
classification, defect detection, smoothing, EDF, probabilistic k-NN classifier
National Category
Probability Theory and Statistics Signal Processing Manufacturing, Surface and Joining Technology
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-161257OAI: oai:DiVA.org:umu-161257DiVA, id: diva2:1333265
Projects
FIQA
Funder
Vinnova, 2015-03706Available from: 2019-07-01 Created: 2019-07-01 Last updated: 2019-07-02

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arXiv:1906.11904

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Pya Arnqvist, NatalyaNilsson, LeifYu, Jun
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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf