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Empirical comparison of defect classifiers on specular surfaces

: Ziebarth, M.

Volltext urn:nbn:de:0011-n-3262819 (852 KByte PDF)
MD5 Fingerprint: d5cd70911baeb034cfd5344f1f03af94
Erstellt am: 10.2.2015

Beyerer, Jürgen (Ed.); Pak, Alexey (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2013. Proceedings : July, 21 - 27, 2013, Triberg-Nussbach in Germany
Karlsruhe: KIT Scientific Publishing, 2014 (Karlsruher Schriften zur Anthropomatik 17)
ISBN: 978-3-7315-0212-8
ISBN: 3-7315-0212-7
DOI: 10.5445/KSP/1000040668
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2013, Triberg-Nussbach>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Today the inspection of specular surfaces, especially in the automobile sector, is often done by humans. An automated inspection would be preferable for reasons such as reproducibility, reliability, and objectivity. However it is problematic to replace humans by machines in this field. The main reasons for this are their greater flexibility for changes in the production process and their ability not only to find defects but to decide whether a customer would complain about those defects. With the deflectometric principle, there is a measurement method for specular surfaces that is fast and accurate enough to compete with humans. Open problems are the necessary expenses for the parameterization of the defect detection and the missing link to the human perception of defects. The first problem is addressed in this paper. An overview of methods capable of detecting and classifying defects of different shapes and scales on unknown surface shapes is given. Then all methods are compared empirically on real measurement data.