  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. 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 Ume{\aa}, Sweden, show that
the proposed approach is much more efficient than the compared methods.
