18857046. SELF-SUPERVISED ANOMALY DETECTION FRAMEWORK FOR VISUAL QUALITY INSPECTION IN MANUFACTRUING (Siemens Aktiengesellschaft)
SELF-SUPERVISED ANOMALY DETECTION FRAMEWORK FOR VISUAL QUALITY INSPECTION IN MANUFACTRUING
Organization Name
Inventor(s)
Baris Erol of Rochester Hills MI US
SELF-SUPERVISED ANOMALY DETECTION FRAMEWORK FOR VISUAL QUALITY INSPECTION IN MANUFACTRUING
This abstract first appeared for US patent application 18857046 titled 'SELF-SUPERVISED ANOMALY DETECTION FRAMEWORK FOR VISUAL QUALITY INSPECTION IN MANUFACTRUING
Original Abstract Submitted
An AI-based method for visual inspection of parts manufactured on a shop floor includes acquiring a set of real images of nominal parts manufactured on the shop floor to create training datasets. A self-supervised pre-trainer module is used to pre-train a loss computation neural network in a self-supervised learning process using a first dataset on pretexts defined by real-world conditions pertaining to the shop floor. The first dataset is labeled by automatically extracting pretext-related information from image metadata. A main anomaly trainer module is used to train a main anomaly detection neural network to reconstruct a nominal part image from an input manufactured part image in an unsupervised learning process using a second dataset. The main anomaly training measures a perceptual loss between an input image and a reconstructed image by measuring a difference between feature representations thereof at one or more layers of the pre-trained loss computation neural network.