Image-based Object Classification of Defects in Steel using Data-driven Machine Learning Optimization

In this paper we study the optimization process of an object classification task for an image-based steel quality measurement system. The goal is to distinguish hollow from solid defects inside of steel samples by using texture and shape features of reconstructed 3D objects. In order to optimize the classification results we propose a holistic machine learning framework that should automatically answer the question "How well do state-of-the-art machine learning methods work for my classification problem?" The framework consists of three layers, namely feature subset selection, feature transform and classifier which subsequently reduce the data dimensionality. A system configuration is defined by feature subset, feature transform function, classifier concept and corresponding parameters. In order to find the configuration with the highest classifier accuracies, the user only needs to provide a set of feature vectors and ground truth labels. The framework performs a totally data-driven optimization using partly heuristic grid search. We incorporate several popular machine learning concepts, such as Principal Component Analysis (PCA), Support Vector Machines (SVM) with different kernels, random trees and neural networks. We show that with our framework even non-experts can automatically generate a ready for use classifier system with a significantly higher accuracy compared to a manually arranged system.