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17 September 2018 Optical surface inspection: A novelty detection approach based on CNN-encoded texture features
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Abstract
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.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Grunwald, Matthias Hermann, Fabian Freiberg, Pascal Laube, and Matthias O. Franz "Optical surface inspection: A novelty detection approach based on CNN-encoded texture features", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107521E (17 September 2018); https://doi.org/10.1117/12.2320657
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