The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 09, 2023

Filed:

Sep. 30, 2020
Applicant:

Ford Global Technologies, Llc, Dearborn, MI (US);

Inventors:

Artem Litvak, San Francisco, CA (US);

Xianling Zhang, San Jose, CA (US);

Nikita Jaipuria, Union City, CA (US);

Shreyasha Paudel, Sunnyvale, CA (US);

Assignee:

FORD GLOBAL TECHNOLOGIES, LLC, Dearborn, MI (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06K 9/62 (2022.01); G08G 1/16 (2006.01); B60W 30/09 (2012.01); B60W 30/095 (2012.01); B60W 60/00 (2020.01); G01C 21/34 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06T 7/70 (2017.01);
U.S. Cl.
CPC ...
G06K 9/6257 (2013.01); B60W 30/09 (2013.01); B60W 30/0956 (2013.01); B60W 60/0015 (2020.02); G01C 21/3461 (2013.01); G06K 9/6215 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/70 (2017.01); G08G 1/16 (2013.01); B60W 2420/42 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30192 (2013.01); G06T 2207/30236 (2013.01); G06T 2207/30252 (2013.01);
Abstract

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a second convolutional neural network (CNN) training dataset by determining an underrepresented object configuration and an underrepresented noise factor corresponding to an object in a first CNN training dataset, generate one or more simulated images including the object corresponding to the underrepresented object configuration in the first CNN training dataset by inputting ground truth data corresponding to the object into a photorealistic rendering engine and generate one or more synthetic images including the object corresponding to the underrepresented noise factor in the first CNN training dataset by processing the simulated images with a generative adversarial network (GAN) to determine a second CNN training dataset. The instructions can include further instructions to train a CNN to using the first and the second CNN training datasets.


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