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:
May. 13, 2025

Filed:

Dec. 14, 2022
Applicant:

Zhejiang University, Hangzhou, CN;

Inventors:

Qiang Yang, Hangzhou, CN;

Chao Su, Hangzhou, CN;

Yuan Cao, Hangzhou, CN;

Di Jiang, Hangzhou, CN;

Hao Xu, Hangzhou, CN;

Kaidi Qiu, Hangzhou, CN;

Assignee:

Zhejiang University, Hangzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/10 (2022.01); G06T 3/4053 (2024.01); G06T 5/20 (2006.01); G06V 10/46 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
G06V 20/176 (2022.01); G06T 3/4053 (2013.01); G06T 5/20 (2013.01); G06V 10/464 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 10/774 (2022.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01);
Abstract

The present disclosure provides a transmission line defect identification method based on a saliency map and a semantic-embedded feature pyramid, including the following steps: step 1: cleaning and classifying a dataset; step 2: generating a super-resolution image for a small target of a transmission line by using an Electric Line-Enhanced Super-Resolution Generative Adversarial Network (EL-ESRGAN) model; step 3: performing image saliency detection on the dataset by constructing a U-Net; step 4: performing data augmentation on the dataset by using GridMask and random cutout algorithms based on a saliency map, and generating a classified dataset; and step 5: performing image classification on a normal set and a defect set by using a ResNet34 classification algorithm and a deep semantic embedding (DSE)-based feature pyramid classification network.


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