US 12,223,632 B2
Intelligent detection method and unmanned surface vehicle for multiple type faults of near-water bridges
Jian Zhang, Nanjing (CN); Zhili He, Nanjing (CN); and Shang Jiang, Nanjing (CN)
Assigned to SOUTHEAST UNIVERSITY, Nanjing (CN)
Appl. No. 17/755,086
Filed by SOUTHEAST UNIVERSITY, Nanjing (CN)
PCT Filed May 8, 2021, PCT No. PCT/CN2021/092393
§ 371(c)(1), (2) Date Apr. 21, 2022,
PCT Pub. No. WO2022/193420, PCT Pub. Date Sep. 22, 2022.
Claims priority of application No. 202110285996.5 (CN), filed on Mar. 17, 2021.
Prior Publication US 2023/0351573 A1, Nov. 2, 2023
Int. Cl. 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)
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)] 6 Claims
OG exemplary drawing
 
1. A method of using an intelligent detection system for detecting multiple types of faults for near-water bridges, comprising
providing the intelligent detection system, comprised of
a first component, an intelligent detection algorithm: CenWholeNet, an infrastructure fault target detection network based on deep learning, being electrically coupled to a second component;
the second component, an embedded parallel attention module PAM into the target detection network CenWholeNet, the parallel attention module includes two sub-modules: a spatial attention sub-module and a channel attention sub-module, being electrically coupled to a third component; and
the third component, an intelligent detection equipment assembly: an unmanned surface vehicle system based on lidar navigation, the unmanned surface vehicle includes four modules, a hull module, a video acquisition module, a lidar navigation module and a ground station module;
a computer readable storage medium, having stored thereon a computer program, said program arranged to:
Step 1: using a primary network to extract features of images;
Step 2: converting the extracted image features, by a detector, into tensor forms required for calculation, and optimizing a result through a loss function;
Step 3: outputting results includes converting the tensor forms into a boundary box and outputting of prediction results of target detection.