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:
Jul. 16, 2024

Filed:

Nov. 15, 2021
Applicant:

Hon Hai Precision Industry Co., Ltd., New Taipei, TW;

Inventors:

Chin-Pin Kuo, New Taipei, TW;

Shih-Chao Chien, New Taipei, TW;

Tung-Tso Tsai, New Taipei, TW;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2023.01); G06N 7/01 (2023.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06F 18/2415 (2023.01); G06F 18/2155 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G06N 7/01 (2023.01); G06T 7/0004 (2013.01); G06T 2207/10004 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30108 (2013.01);
Abstract

A method for detecting defects in multi-scale images and a computing device applying the method acquires a to-be-detected image and converts the to-be-detected image into a plurality of target images of preset sizes. Feature extraction is performed on each target image by using a pre-trained encoder to obtain a latent vector, the latent vector of each target image is inputted into a decoder corresponding to the encoder to obtain a reconstructed image and then into a pre-trained Gaussian mixture model to obtain an estimated probability. Reconstruction error is calculated according to each target image and the corresponding reconstructed image. A total error is calculated according to the reconstruction error of each target image and the corresponding estimated probability, and a detection result is determined according to the total error of each target image and a corresponding preset threshold, thereby improving an accuracy of defect detection.


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