Deep anomaly detection for endoscopic inspection of cast iron parts

Detecting anomalies in image data plays a key role in automated industrial quality control. For this purpose, machine learning methods have proven useful for image processing tasks. However, supervised machine learning methods are highly dependent on the data with which they have been trained. In industrial environments data of defective samples are rare. In addition, the available data are often biased towards specific types, shapes, sizes, and locations of defects. On the contrary, one-class classification (OCC) methods can solely be trained with normal data which are usually easy to obtain in large quantities. In this work we evaluate the applicability of advanced OCC methods for an industrial inspection task. Convolutional Autoencoders and Generative Adversarial Networks are applied and compared with Convolutional Neural Networks. As an industrial use case we investigate the endoscopic inspection of cast iron parts. For the use case a dataset was created. Results show that both GAN and autoencoder-based OCC methods are suitable for detecting defective images in our industrial use case and perform on par with supervised learning methods when few data are available.

DDC Class

004: Informatik

600: Technik

620: Ingenieurwissenschaften

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Verfahren zur automatischen Qualitätskontrolle mittels robotergeführten Videoendoskopen

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https://creativecommons.org/licenses/by/4.0/

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2022_Schmedemann_Deep Anomaly Detection for Endoscopic Inspection of Cast Iron Parts.pdf

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Deep anomaly detection for endoscopic inspection of cast iron parts

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