IP Library › Granted Patent US 11,908,175
Granted Patent B2
US 11,908,175 · App. 17/693,095 · Granted Feb 20, 2024

Electronic device training image recognition model and operation method for same

Inventors: Seowoo Jang (Suwon-si, KR); Sangung Yi (Suwon-si, KR)
Assignee: SAMSUNG ELECTRONICS CO., LTD.
G06V10/7747G06V10/945
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Quick Facts
Patent No.
US 11,908,175
App. No.
17/693,095
Granted
Feb 20, 2024
Kind
B2
Abstract

A method of training an image recognition model includes: generating a virtual dynamic vision sensor (DVS) image using a virtual simulator; generating label information including information about a correct answer to a result of recognition of the DVS image by the image recognition model, with respect to the DVS image; and training the image recognition model by modifying the image recognition model so that a difference between the result of recognition of the DVS image by the image recognition model and the label information is reduced.

Claims (24)

1. A method of training an image recognition model, the method comprising:

generating a virtual dynamic vision sensor (DVS) image using a virtual simulator;

generating label information comprising information about a correct answer to a result of recognition of the DVS image by the image recognition model with respect to the DVS image; and

training the image recognition model by modifying the image recognition model so that a difference between the result of recognition of the DVS image by the image recognition model and the label information is reduced.

2. The method of claim 1 , wherein the virtual DVS image is generated based on a virtual environment generated by the virtual simulator, and a virtual object placed in the virtual environment.

3. The method of claim 2 , wherein the label information is obtained based on predefined characteristic information with respect to at least one of the virtual environment or the virtual object.

4. The method of claim 2 , wherein, in a real environment corresponding to the virtual environment, an object capable of being captured as a real DVS image, which is capable of being recognized by the image recognition model, is placed in the virtual environment as the virtual object.

5. The method of claim 1 , wherein the generating of the virtual DVS image comprises:

determining at least one camera view point in the virtual environment generated by the virtual simulator; and

generating at least one virtual DVS image simultaneously captured from the at least one camera view point,

wherein the image recognition model is trained based on the at least one virtual DVS image.

6. The method of claim 1 , wherein, a virtual environment is generated by the virtual simulator based on information about a surrounding environment in which a real DVS image capable of being recognized by the image recognition model is capable of being captured, and the virtual DVS image is generated based on the virtual environment.

7. The method of claim 6 , wherein, based on the information about the surrounding environment changing by more than a reference value, the virtual DVS image is generated based on the changed information about the surrounding environment.

8. A non-transitory computer-readable recording medium having recorded thereon a program which, when executed by a processor of an electronic device, cause the electronic device to perform operations including the method of claim 1 .

9. An electronic device configured to train an image recognition model, the electronic device comprising:

a memory storing the image recognition model; and

at least one processor configured to: generate a virtual dynamic vision sensor (DVS) image using a virtual simulator, generate label information comprising information about a correct answer to a result of recognition of the DVS image by the image recognition model with respect to the DVS image, and train the image recognition model by modifying the image recognition model so that a difference between the result of recognizing the DVS image by the image recognition model and the label information is reduced.

10. The electronic device of claim 9 , wherein the virtual DVS image is generated based on a virtual environment generated by the virtual simulator and a virtual object placed in the virtual environment.

11. The method of claim 10 , wherein the label information is obtained based on predefined characteristic information with respect to at least one of the virtual environment or the virtual object.

12. The electronic device of claim 10 , wherein, in a real environment corresponding to the virtual environment, an object capable of being captured as a real DVS image, which is capable of being recognized by the image recognition model, is placed in the virtual environment as the virtual object.

13. The electronic device of claim 9 , wherein the at least one processor is further configured to: determine at least one camera view point in the virtual environment generated by the virtual simulator and generate at least one virtual DVS image simultaneously captured from the at least one camera view point,

wherein the image recognition model is trained based on the at least one virtual DVS image.

14. The electronic device of claim 9 , wherein, a virtual environment is generated by the virtual simulator based on information about a surrounding environment in which a real DVS image capable of being recognized by the image recognition mode is capable of being captured, and the virtual DVS image is generated based on the virtual environment.

15. The electronic device of claim 14 , wherein, based on the information about the surrounding environment changing by more than a reference value, the virtual DVS image is generated based on the changed information about the surrounding environment.

Assignments (2)
EMPLOYMENT AGREEMENT Recorded May 2, 2022
From: YI, SANGUNG
To: SAMSUNG ELECTRONICS CO., LTD.
Reel/Frame 060813/0788 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 11, 2022
From: JANG, SEOWOO
To: SAMSUNG ELECTRONICS CO., LTD.
Reel/Frame 059246/0436 →
Priority Claims (1)
KR 10-2019-0113023 · Sep 11, 2019 · national
Continuity (2)
Continuation PCTKR2020004610 · Apr 6, 2020
Related Publication 20220198786A1 · Jun 23, 2022