Automatic classification of severe and mild wear in worn surface images using histograms of oriented gradients as descriptor

The main objective of this work was to develop a novel computational strategy that is able to predict wear regime operation in worn surfaces. The image data were taken from worn surfaces images of cast iron specimens subjected to abrasion wear tests. These images were classified into two groups, identified with the severe and mild labels, according to the wear rate results found during the wear tests. The surface features of worn surfaces images were coded as a dense Histogram of Oriented Gradient (HOG) descriptor and thus classifier models were herein implemented to obtain a learning model of wear severity. Gaussian Naive Bayes, Decision Tree and Random Forest were the classifier models used, which span the family of classifiers from fast to robust implementations. An evaluation of the classifier capacity to identify those images corresponding to the severe and mild wear regimes was made by following a k-fold cross validation strategy. The qualitative characterization of worn surfaces images through the HOG computation and the application of classifier models allow predicting well whether a mild or a severe abrasive wear regimes operated. The proposed approach achieves more than 80% of accuracy in almost all HOG configuration and for the different classifiers herein evaluated.