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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-classdoi:10.15480/882.4639 fatcat:degd7cnhejdmppfx5jwith2pb4