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. 23, 2025

Filed:

Jun. 09, 2025
Applicant:

Hunan University, Changsha, CN;

Inventors:

Jianxu Mao, Changsha, CN;

Junfei Yi, Changsha, CN;

Yaonan Wang, Changsha, CN;

Hui Zhang, Changsha, CN;

Kai Zeng, Changsha, CN;

Ziming Tao, Changsha, CN;

He Xie, Changsha, CN;

Caiping Liu, Changsha, CN;

Qing Zhu, Changsha, CN;

Min Liu, Changsha, CN;

Xian'en Zhou, Changsha, CN;

Assignee:

Hunan University, Changsha, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); B64F 5/60 (2017.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0004 (2013.01); B64F 5/60 (2017.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06T 2207/20016 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Disclosed in the present invention is a neural network-based defect detection method for gluing quality on aircraft skin. The method includes: data acquisition: taking photos of aircraft skin by using a camera to acquire image data; preprocessing the acquired image data; annotating the data by using annotation software to acquire a data set for network training; establishing a defect detection network model based on feature erasure and boundary refinement, where the defect detection network model includes a feature extraction network, a semantic-guided feature erasure module, a multi-scale feature fusion network, and a defect prediction network based on boundary refinement, which are sequentially connected, the data set is used for training the network model, and trained model parameters are saved; and detecting a directly collected skin gluing image by using the trained network model and outputting detection results.


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