US 12,277,696 B2
Data augmentation for domain generalization
Laura Beggel, Stuttgart (DE); Filipe J. Cabrita Condessa, Pittsburgh, PA (US); Robin Hutmacher, Renningen (DE); Jeremy Kolter, Pittsburgh, PA (US); Nhung Thi Phuong Ngo, Karlsruhe (DE); Fatemeh Sheikholeslami, Pittsburgh, PA (US); and Devin T. Willmott, Pittsburgh, PA (US)
Assigned to Robert Bosch GmbH, (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Apr. 8, 2022, as Appl. No. 17/716,590.
Prior Publication US 2023/0326005 A1, Oct. 12, 2023
Int. Cl. G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01)
CPC G06T 7/0004 (2013.01) [G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for performing at least one task with autonomous control of a part, the system comprising:
an image sensor configured to output an image of the part;
an actuator configured to bin the part based on a detected defect in the part;
a processor; and
memory including instructions that, when executed by the processor, cause the processor to:
utilize an image segmenter on a source image stored in the memory to generate a source image segmentation mask having a foreground region and a background region;
utilize the image segmenter on a target image stored in the memory to generate a target image segmentation mask having a foreground region and a background region;
determine a source image foreground and a source image background of the source image based on the source image segmentation mask;
determine a target image foreground and a target image background of the target image based on the target image segmentation mask;
remove the target image foreground from the target image;
insert the source image foreground into the target image with the removed target image foreground to create an augmented image having the source image foreground and the target image background;
update training data of the machine learning model with the augmented image;
utilize the machine learning model with the updated training data to determine a defect in the part; and
actuate the actuator to bin the part based on the determined defect in the part.