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:
Sep. 05, 2023

Filed:

Aug. 26, 2020
Applicant:

Goertek Inc., Shandong, CN;

Inventors:

Jie Liu, Shandong, CN;

Li Ma, Shandong, CN;

Liang Zhang, Shandong, CN;

Assignee:

GOERTEK INC., Shandong, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06T 7/136 (2017.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G01N 21/88 (2006.01); G06N 3/08 (2023.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06F 18/2413 (2023.01); G06V 10/42 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/001 (2013.01); G01N 21/8851 (2013.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06F 18/24133 (2023.01); G06N 3/08 (2013.01); G06T 7/0004 (2013.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06T 7/70 (2017.01); G06V 10/431 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G01N 2021/8854 (2013.01); G01N 2021/8887 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

A product defect detection method, device and system are disclosed. The product defect detection method comprises: constructing a defect detection framework including a classification network, a locating detection network and a judgment network; training the classification network by using a sample image of a product containing different defect types to obtain a classification network capable of classifying the defect types existing in the sample image; training the locating detection network by using a sample image of a product containing different defect types to obtain a locating detection network capable of locating a position of each type of defect in the sample image; inputting an acquired product image into the defect detection framework, inputting a classification result and a detection result obtained into the judgment network to judge whether the product has a defect, and detecting a defect type and a defect position when the product has a defect.


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