Intelligent detection method and unmanned surface vehicle for multiple type faults of near-water bridges

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United States of America

PATENT NO 12223632
APP PUB NO 20230351573A1
SERIAL NO

17755086

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Abstract

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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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Patent Owner(s)

  • SOUTHEAST UNIVERSITY

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
He, Zhili Nanjing, CN 2 0
Jiang, Shang Nanjing, CN 1 0
Zhang, Jian Nanjing, CN 1504 17611

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