A Rapid Crack Detection Technique Based on Attention for Intelligent M&amp;O of Cross-Sea Bridge

Yong-chuan ZHOU; Guang-jun LI; Wei WEI; Ya-meng WANG; Qiang JING

doi:10.1007/s13344-024-0068-0

Citation: Yong-chuan ZHOU, Guang-jun LI, Wei WEI, Ya-meng WANG and Qiang JING. A Rapid Crack Detection Technique Based on Attention for Intelligent M&O of Cross-Sea Bridge[J]. China Ocean Engineering, 2024, 38(5): 866-876. doi: 10.1007/s13344-024-0068-0 shu

A Rapid Crack Detection Technique Based on Attention for Intelligent M&O of Cross-Sea Bridge

1.
Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai 519015, China
2.
National Observation and Research Station of Material Corrosion and Structural Safety of Hong Kong-Zhuhai-Macao Bridge in Guangdong, Zhuhai 519000, China
3.
Wuhan Maritime Communication Research Institute, Wuhan 430079, China

Corresponding author: Wei WEI, 510320796@qq.com
Received Date: 2024-02-21
Accepted Date: 2024-05-06
Available Online: 2024-10-22

Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects. In practice, however, this task faces the challenge of finding a balance between detection accuracy and efficiency. To alleviate this problem, a lightweight and efficient real-time crack segmentation framework was developed. Specifically, in the network model system based on an encoding-decoding structure, the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers, and in the decoding process, we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features. Codecs are connected by pyramid pooling model (PPM) filtering. The results show that the crack segmentation accuracy and real-time operation capability larger than 76% and 15 fps, respectively, are validated by three publicly available datasets. These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.
- bridge defect detection,
- crack detection,
- lightweight design
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