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Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers

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  • Pya Arnqvist, Natalya
  • Ngendangenzwa, Blaise
  • Lindahl, Eric
  • Nilsson, Leif
  • Yu, Jun

Abstract

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 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 rate when applying standard learning classifiers. In addition, probability based performance evaluation metrics have been proposed 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.

Suggested Citation

  • Pya Arnqvist, Natalya & Ngendangenzwa, Blaise & Lindahl, Eric & Nilsson, Leif & Yu, Jun, 2021. "Efficient surface finish defect detection using reduced rank spline smoothers and probabilistic classifiers," Econometrics and Statistics, Elsevier, vol. 18(C), pages 89-105.
  • Handle: RePEc:eee:ecosta:v:18:y:2021:i:c:p:89-105
    DOI: 10.1016/j.ecosta.2020.05.005
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    References listed on IDEAS

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    1. Wu, Ximing & Sickles, Robin, 2018. "Semiparametric estimation under shape constraints," Econometrics and Statistics, Elsevier, vol. 6(C), pages 74-89.
    2. C. C. Holmes & N. M. Adams, 2002. "A probabilistic nearest neighbour method for statistical pattern recognition," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 295-306, May.
    3. Cucala, Lionel & Marin, Jean-Michel & Robert, Christian P. & Titterington, D. M., 2009. "A Bayesian Reassessment of Nearest-Neighbor Classification," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 263-273.
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