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. 27, 2025

Filed:

May. 06, 2022
Applicant:

Anhui University, Hefei, CN;

Inventors:

Jie Chen, Hefei, CN;

Runfan Xia, Hefei, CN;

Zhixiang Huang, Hefei, CN;

Huiyao Wan, Hefei, CN;

Xiaoping Liu, Hefei, CN;

Zihan Cheng, Hefei, CN;

Bocai Wu, Hefei, CN;

Baidong Yao, Hefei, CN;

Zheng Zhou, Hefei, CN;

Jianming Lv, Hefei, CN;

Yun Feng, Hefei, CN;

Wentian Du, Hefei, CN;

Jingqian Yu, Hefei, CN;

Assignee:

Anhui University, Hefei, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01S 13/90 (2006.01); G06T 3/4046 (2024.01); G06T 7/60 (2017.01); G06T 7/73 (2017.01); G06V 10/22 (2022.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/77 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G01S 13/9027 (2019.05); G06T 3/4046 (2013.01); G06T 7/60 (2013.01); G06T 7/73 (2017.01); G06V 10/225 (2022.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/7715 (2022.01); G06V 10/806 (2022.01); G06V 10/82 (2022.01); G06T 2207/10044 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06V 2201/07 (2022.01);
Abstract

A contextual visual-based synthetic-aperture radar (SAR) target detection method and apparatus, and a storage medium, belonging to the field of target detection is described. The method includes: obtaining an SAR image; and inputting the SAR image into a target detection model, and positioning and recognizing a target in the SAR image by using the target detection model, to obtain a detection result. In the present disclosure, a two-way multi-scale connection operation is enhanced through top-down and bottom-up attention, to guide learning of dynamic attention matrices and enhance feature interaction under different resolutions. The model can extract the multi-scale target feature information with higher accuracy, for bounding box regression and classification, to suppress interfering background information, thereby enhancing the visual expressiveness. After the attention enhancement module is added, the detection performance can be greatly improved with almost no increase in the parameter amount and calculation amount of the whole neck.


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