Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction

During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.