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:
Aug. 29, 2023

Filed:

Aug. 26, 2020
Applicant:

Goertek Inc., Shandong, CN;

Inventors:

Jie Liu, Shandong, CN;

Jifeng Tian, Shandong, CN;

Fuli Xie, Shandong, CN;

Shunran Di, Shandong, CN;

Yifan Zhang, Shandong, CN;

Assignee:

GOERTEK INC., Shandong, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/70 (2022.01); G06T 7/00 (2017.01); G01N 21/88 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0004 (2013.01); G01N 21/8851 (2013.01); G06V 10/70 (2022.01); G06T 2207/10004 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30108 (2013.01);
Abstract

A product defect detection method, device and system are disclosed. The method comprises: acquiring a sample image of a product, extracting candidate image blocks probably including a product defect from the sample image, and extracting preset shape features corresponding to the candidate image blocks and texture features corresponding to the candidate image blocks; training a first-level classifier using the preset shape features to obtain a first-level classifier that can further screen out target image blocks probably including a product defect from the candidate image blocks; training a second-level classifier using the texture features to obtain a second-level classifier that can correctly identify a product defect; and when performing product defect detection, inputting preset shape features of candidate image blocks extracted from a product image into the first-level classifier, and then inputting texture features of obtained target image blocks into the second-level classifier to detect a defect in the product.


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