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:
Apr. 25, 2023

Filed:

Jan. 26, 2022
Applicant:

Guangdong University of Technology, Guangzhou, CN;

Inventors:

Renquan Lu, Guangzhou, CN;

Yong Xu, Guangzhou, CN;

Hongxia Rao, Guangzhou, CN;

Chang Liu, Guangzhou, CN;

Hui Chen, Guangzhou, CN;

Yongmin Luo, Guangzhou, CN;

Hui Peng, Guangzhou, CN;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/73 (2017.01); G06V 20/40 (2022.01); B64C 39/02 (2023.01); G06T 7/246 (2017.01); G06T 7/277 (2017.01); G06V 20/17 (2022.01); G06V 10/774 (2022.01); G06V 10/80 (2022.01); G06V 10/77 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G05D 1/04 (2006.01); G06T 7/60 (2017.01);
U.S. Cl.
CPC ...
B64C 39/024 (2013.01); G05D 1/042 (2013.01); G06T 7/246 (2017.01); G06T 7/277 (2017.01); G06T 7/60 (2013.01); G06T 7/73 (2017.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/7715 (2022.01); G06V 10/7747 (2022.01); G06V 10/806 (2022.01); G06V 20/17 (2022.01); G06V 20/41 (2022.01); G06V 20/46 (2022.01); B64U 2201/00 (2023.01); G06T 2207/10016 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01); G06V 2201/07 (2022.01);
Abstract

A landing tracking control method comprises the following contents: a tracking model training stage and an unmanned aerial vehicle real-time tracking stage. The landing tracking control method extracts a network Snet by using a lightweight feature and makes modification, so that an extraction speed of the feature is increased to better meet a real-time requirement. Weight allocation on the importance of channel information is carried out to differentiate effective features more purposefully and utilize the features, so that the tracking precision is improved. In order to improve a training effect of the network, a loss function of an RPN network is optimized, a regression precision of a target frame is measured by using CIOU, and meanwhile, calculation of classified loss function is adjusted according to CIOU, and a relation between a regression network and classification network is enhanced.


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