Intelligent crack detection based on attention mechanism in convolution neural network

• Published in: Advances in structural engineering 2021-07, Vol.24 (9), p.1859-1868
• Main Authors: Cui, Xiaoning, Wang, Qicai, Dai, Jinpeng, Xue, Yanjin, Duan, Yun
• Format: Article
• Language: English
• ISSN: 1369-4332; 2048-4011
• DOI: 10.1177/1369433220986638

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.