This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact
image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality
inspection at the end of the production line aims to classify five different glass defect types. The defect images are
usually characterized by very little ‘image structure’, i.e. homogeneous regions without distinct image texture.
Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect
classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based
features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and
texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution
of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate
classification approaches for this specific class of images. In this way, the following machine learning algorithms were
compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural
network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect
images and applied cross validation for evaluation purposes.