The publication “Evaluation of Deep Learning for Semantic Image Segmentation in Tool Condition Monitoring” by Benjamin Lutz, Dominik Kisskalt, Daniel Regulin, Raven Reisch, Prof. Dr. Andreas Schiffler and Prof. Dr.-Ing. Jörg Franke was awarded with the Best Paper Award of the Special Session Advanced Machine Vision, which took place in the context of the 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019) from 16th to 19th December 2019 in Boca Raton, Florida.
The awarded publication presents an tool condition monitoring approach using machine learning. With artificial neural networks, image data of worn tools are analyzed and image segmentation is carried out. Thus, the detection and pixel accurate localization of different types of wear defects is possible.