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:
Jul. 04, 2023

Filed:

Mar. 15, 2023
Applicant:

Ludong University, Yantai, CN;

Inventors:

Jun Yue, Yantai, CN;

Yifei Zhang, Yantai, CN;

Qing Wang, Yantai, CN;

Zhenbo Li, Beijing, CN;

Guangjie Kou, Yantai, CN;

Jun Zhang, Yantai, CN;

Shixiang Jia, Yantai, CN;

Ning Li, Yantai, CN;

Assignee:

Ludong University, Yantai, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06N 3/00 (2023.01); G06V 10/80 (2022.01); G06V 40/10 (2022.01); G06V 10/26 (2022.01);
U.S. Cl.
CPC ...
G06V 10/806 (2022.01); G06V 10/267 (2022.01); G06V 40/10 (2022.01); G06V 2201/07 (2022.01);
Abstract

Disclosed is a method for detectingcantor under intra-class occulusion based on cross-scale layered feature fusion, including image collecting, image processing and network model, where collected images are labeled, image sizes are adjusted to obtain input images, and the input images are input into an object detection network, integrated by convolution and inserted into cross-scale layered feature fusion modules, characterized by including dividing all features input into the cross-scale layered feature fusion modules into n layers, composed of s feature mapping subsets, and fusing features of each feature mapping subset with that of other feature mapping subsets, and connecting; carrying out convolution operation, outputting training result; adjusting network parameters by a loss function to obtain parameters for a network model; inputting final output candidate boxes into a non-maximum suppression module to screen correct prediction boxes, so that prediction result is obtained.


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