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:
Feb. 11, 2025

Filed:

May. 08, 2021
Applicant:

Southeast University, Nanjing, CN;

Inventors:

Jian Zhang, Nanjing, CN;

Zhili He, Nanjing, CN;

Shang Jiang, Nanjing, CN;

Assignee:

SOUTHEAST UNIVERSITY, Nanjing, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); B63B 1/12 (2006.01); B63B 35/00 (2020.01); B63B 45/04 (2006.01); B63B 79/40 (2020.01); G01M 5/00 (2006.01); G06T 7/73 (2017.01); H04N 23/56 (2023.01);
U.S. Cl.
CPC ...
G06T 7/0002 (2013.01); B63B 1/125 (2013.01); B63B 35/00 (2013.01); B63B 45/04 (2013.01); B63B 79/40 (2020.01); G01M 5/0008 (2013.01); G01M 5/0075 (2013.01); G01M 5/0091 (2013.01); G06T 7/73 (2017.01); B63B 2035/008 (2013.01); B63B 2211/02 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30184 (2013.01); G06T 2207/30252 (2013.01); H04N 23/56 (2023.01);
Abstract

The invention discloses an intelligent detection method for multiple types of faults for near-water bridges and an unmanned surface vehicle. The method includes an infrastructure fault target detection network CenWholeNet and a bionics-based parallel attention module PAM. CenWholeNet is a deep learning-based Anchor-free target detection network, which mainly comprises a primary network and a detector, used to automatically detect faults in acquired images with high precision. Wherein, the PAM introduces an attention mechanism into the neural network, including spatial attention and channel attention, which is used to enhance the expressive power of the neural network. The unmanned surface vehicle includes hull module, video acquisition module, lidar navigation module and ground station module, which supports lidar navigation without GPS information, long-range real-time video transmission and highly robust real-time control, used for automated acquisition of information from bridge underside.


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