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Efficient surface finish defect detection using reduced rank spline smoothers
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)In: CRoNoS & MDA 2019, 2019Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

One of the primary concerns of product quality control in the automotive industry is an automated 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 neighbor probabilistic classifier. Rather than analyzing the natural images of the car body surfaces, the deflectometry 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 us to reach near zero misclassification error when applying standard learning classifiers. We also propose the 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 cab plant in Umea, Sweden, show that the proposed approach is much more efficient than compared methods.

Place, publisher, year, edition, pages
2019.
National Category
Probability Theory and Statistics Signal Processing
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-158014OAI: oai:DiVA.org:umu-158014DiVA, id: diva2:1303629
Conference
CRoNoS & MDA2019, Cyprus, April 14-16, 2019
Projects
FIQA
Funder
Vinnova, 2015-03706Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-04-16Bibliographically approved

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http://cmstatistics.org/CRONOSMDA2019/docs/BoA_CRONOSMDA2019.pdf?20190324031000http://cmstatistics.org/CRONOSMDA2019/

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Pya Arnqvist, NatalyaNgendangenzwa, BlaiseNilsson, LeifYu, Jun

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CiteExportLink to record
Permanent link

Direct link
Cite
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