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Optical surface inspection: A novelty detection approach based on CNN-encoded texture features

  • In inspection systems for textured surfaces, a reference texture is typically known before novel examples are inspected. Mostly, the reference is only available in a digital format. As a consequence, there is no dataset of defective examples available that could be used to train a classifier. We propose a texture model approach to novelty detection. The texture model uses features encoded by a convolutional neural network (CNN) trained on natural image data. The CNN activations represent the specific characteristics of the digital reference texture which are learned by a one-class classifier. We evaluate our novelty detector in a digital print inspection scenario. The inspection unit is based on a camera array and a flashing light illumination which allows for inline capturing of multichannel images at a high rate. In order to compare our results to manual inspection, we integrated our inspection unit into an industrial single-pass printing system.

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Metadaten
Author:Michael Grunwald, Matthias Hermann, Fabian Freiberg, Pascal Laube, Matthias O. FranzORCiDGND
DOI:https://doi.org/10.1117/12.2320657
Parent Title (English):Applications of Digital Image Processing XLI, 19-23 August 2018, San Diego, California (Proceedings of SPIE Optical Engineering & Applications, Vol. 10752)
Document Type:Conference Proceeding
Language:English
Year of Publication:2018
Release Date:2019/01/08
Pagenumber:13
Institutes:Institut für Optische Systeme - IOS
Open Access?:Nein
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)