US 12,482,244 B2
Active learning management system for automated inspection systems
Samuel S. Schreiner, New Brighton, MN (US); Steven P. Floeder, Shoreview, MN (US); Jeffrey P. Adolf, Rochester, MN (US); Carl J. Skeps, Lakeville, MN (US); and Shane T. Van Kampen, Cottage Grove, MN (US)
Assigned to 3M Innovative Properties Company, St. Paul, MN (US)
Appl. No. 18/269,657
Filed by 3M INNOVATIVE PROPERTIES COMPANY, St. Paul, MN (US)
PCT Filed Dec. 23, 2021, PCT No. PCT/IB2021/062274
§ 371(c)(1), (2) Date Jun. 26, 2023,
PCT Pub. No. WO2022/144733, PCT Pub. Date Jul. 7, 2022.
Claims priority of provisional application 63/131,166, filed on Dec. 28, 2020.
Prior Publication US 2024/0071059 A1, Feb. 29, 2024
Int. Cl. G06V 10/778 (2022.01); G06V 10/77 (2022.01)
CPC G06V 10/778 (2022.01) [G06V 10/7715 (2022.01)] 19 Claims
OG exemplary drawing
 
1. A method for selecting product images for training a machine-learning model to inspect images of a product, the method comprising:
obtaining, by an image gatherer of an active learning framework from an image repository, product images to include in an image population;
receiving, by an image selector, an indication of an image selection strategy from a plurality of image selection strategies, each of the image selection strategies defining selection operations for determining if a product image is to be included in a set of images of interest;
instantiating, by the image selector, a selection strategy task process corresponding to the image selection strategy, the selection strategy task process configured to perform the selection operations of the image selection strategy;
determining, by the selection strategy task process, one or more image transforms from a plurality of image transforms based on configuration data for the indicated image selection strategy;
in response to determining that one or more of the product images in the image population have not been previously transformed according to the one or more image transforms, instantiating one or more image transform task processes corresponding to the one or more image transforms, wherein the one or more image transform task processes are configured to perform image manipulation operations to obtain the transformed image data for each of the one or more of the product images in the image population;
selecting, by the selection strategy task process, a subset of images from the image population for inclusion in the set of images of interest based on the selection operations for the indicated image selection strategy and the transformed image data;
determining, by an image labeler, an indication of one or more descriptive labels and applying the one or more descriptive labels to the respective sets of images; and
training an inspection model for a product inspection system based on the set of images of interest and corresponding labels of the set of images of interest.