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. 02, 2025

Filed:

Jan. 13, 2023
Applicant:

Xi'an Jiaotong University, Xi'an, CN;

Inventors:

Shuo Wang, Xi'an, CN;

Jing Liu, Xi'an, CN;

Tonghai Wu, Xi'an, CN;

Miao Wan, Xi'an, CN;

Yaguo Lei, Xi'an, CN;

Junyi Cao, Xi'an, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/77 (2022.01); G06N 3/0455 (2023.01); G06N 3/048 (2023.01); G06T 5/00 (2024.01); G06T 5/50 (2006.01); G06T 5/70 (2024.01); G06T 7/00 (2017.01); G06T 7/73 (2017.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 10/7715 (2022.01); G06N 3/0455 (2023.01); G06N 3/048 (2023.01); G06T 5/50 (2013.01); G06T 5/70 (2024.01); G06T 7/001 (2013.01); G06T 7/74 (2017.01); G06V 10/7796 (2022.01); G06V 10/82 (2022.01); G06T 2207/20192 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30164 (2013.01);
Abstract

A method and system of enhancing online reflected light ferrograph images. The method includes: based on contour markers of wear particles in the online reflected light ferrograph image, performing concatenate fusion on the SqueezeNet-Unet-based wear particle position prediction network and the ResNeXt-CycleGAN image transformation network to construct an online reflected light ferrograph image enhancement model; determining loss function of the position prediction network; combining SSIM and L1 losses to optimize cycle-consistency loss function of the ResNeXt-CycleGAN image transformation network; designing overall loss function of the ferrograph image enhancement model by weighted fusion; and optimizing the ferrograph image enhancement model with the overall loss function as optimization object successively using a training sample set consisting of an original online reflected light ferrograph image and a traditional algorithm-enhanced online reflected light ferrograph image and a training sample set consisting of the original image and an offline reflected light ferrograph image.


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