Automatic classification of seam pucker images based on ordinal quality grades

Seam pucker evaluation is based on the annotation of seam specimens into discrete grades of quality presenting an ordinal arrangement. Therefore, an ordinal logistic regression (OLR) model is highly qualified to fit the features extracted by such specimens into the assigned quality grades. This work focuses on building an automatic system for seam pucker evaluation based on an OLR model and comparing its performance with a similar system that employs an ordinary least squares (OLS) regression model. In this direction, three separate types of features have been extracted by a dataset of 325 seam images. The OLR model outperformed OLS for all feature types showing that its theoretical advantage applies also in practice. The best OLR model produced a correct classification rate of 81.2%, matching up to the performance of human experts.