US 12,333,705 B2
Method and apparatus for analyzing a product, training method, system, computer program, and computer-readable storage medium
Felix Rothmund, Munich (DE); Simran Agarwal, Munich (DE); Leslie Casas, Munich (DE); Keng Chai, Munich (DE); Markus Bößl, Munich (DE); Shweta Mahajan, Munich (DE); Jonathan Pirnay, Munich (DE); and Jochen Riedisser, Munich (DE)
Assigned to Fujitsu Technology Solutions GmbH, Munich (DE)
Filed by Fujitsu Technology Solutions GmbH, Munich (DE)
Filed on Jul. 19, 2022, as Appl. No. 17/868,044.
Claims priority of application No. 102021118711.7 (DE), filed on Jul. 20, 2021.
Prior Publication US 2023/0022631 A1, Jan. 26, 2023
Int. Cl. G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06T 7/0008 (2013.01) [G06T 7/0012 (2013.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/7747 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30108 (2013.01); G06V 2201/06 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A method of analyzing a product, the method comprising:
receiving an image of the product;
performing an anomaly detection on the received image using an inpainting autoencoder, wherein the inpainting autoencoder comprises at least one first neural network trained based on a first set of training images, and the first set of training images comprises a plurality of training images each showing a corresponding defect-free product;
determining, using a binary classifier, whether or not a defect is present based on a result of the anomaly detection;
performing defect detection on the received image using a defect detector, wherein the defect detector comprises at least one third neural network trained based on at least one third set of training images, and the at least one third set of training images comprises a plurality of training images each showing a corresponding defective product; and
evaluating a result of the analysis of the received image based on a weighting of the results of the anomaly detection, the defect detection, and the binary classifier.
 
6. A method of training an apparatus for analyzing a product, the method comprising:
creating a first set of training images, the first set of training images comprising a plurality of training images each showing a defect-free product;
training at least a first neural network of an inpainting autoencoder based on the first set of training images;
creating a binary classifier configured to identify whether or not a defect is present based on a result of an anomaly detection using the inpainting autoencoder;
creating at least one third set of training images, wherein the at least one third set of training images comprises a plurality of training images each showing a defective product, and further at least one defect is marked in each training image of the third set of training images;
training at least one third neural network of a defect identifier based on the at least one third set of training images; and
determining a weighting by which to evaluate results of an anomaly detection by the inpainting autoencoder, a defect detection by the defect detector, and an application of the binary classifier.