公开(公告)号：US20230351573A1

公开(公告)日：2023-11-02

申请号：US17755086

申请日：2021-05-08

Applicant: SOUTHEAST UNIVERSITY

Inventor： JIAN ZHANG ,  Zhili HE ,  Shang Jiang

IPC: G06T7/00 ,  G01M5/00 ,  G06T7/73 ,  B63B1/12 ,  B63B35/00 ,  B63B45/04 ,  B63B79/40

CPC classification number: G06T7/0002 ,  G01M5/0008 ,  G01M5/0091 ,  G01M5/0075 ,  G06T7/73 ,  B63B1/125 ,  B63B35/00 ,  B63B45/04 ,  B63B79/40 ,  G06T2207/30252 ,  G06T2207/20084 ,  G06T2207/30184 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/20081 ,  H04N23/56

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.

公开(公告)号：US12223632B2

公开(公告)日：2025-02-11

申请号：US17755086

申请日：2021-05-08

Applicant: SOUTHEAST UNIVERSITY

Inventor： Jian Zhang ,  Zhili He ,  Shang Jiang

IPC: G06T7/00 ,  B63B1/12 ,  B63B35/00 ,  B63B45/04 ,  B63B79/40 ,  G01M5/00 ,  G06T7/73 ,  H04N23/56

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.