期刊
ADVANCES IN STRUCTURAL ENGINEERING
卷 24, 期 9, 页码 1859-1868出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1369433220986638
关键词
attention mechanism; convolutional neural network; crack detection; deep learning; semantic segmentation; structural health monitoring
资金
- National Science Foundation of China [51768033]
In this study, the proposed Att-Unet model achieved better results in crack segmentation tasks, showing improved accuracy, precision, and F1-scores. Att-Unet effectively extracts multi-scale features of cracks, focuses on critical areas, and reconstructs semantics to improve crack segmentation capability.
The intelligent detection of distress in concrete is a research hotspot in structural health monitoring. In this study, Att-Unet, an improved attention-mechanism fully convolutional neural network model, was proposed to realize end-to-end pixel-level crack segmentation. Att-Unet consists of three parts: encoding module, decoding module, and AG (Attention Gate) module. The benefits associated with this module can effectively extract multi-scale features of cracks, focus on critical areas, and reconstruct semantics, to significantly improve the crack segmentation capability of the Att-Unet model. On the same data set, the mainstream semantic segmentation models (FCN and Unet) were trained simultaneously. Upon comparing and analyzing the calculated results of Att-Unet model with those of FCN and Unet, the results are as follows: for crack images under different conditions, Att-Unet achieved better results in accuracy, precision and F1-scores. Besides, Att-Unet showed higher feature extraction accuracy and better generalization ability in the crack segmentation task.
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