Intelligent crack detection based on attention mechanism in convolution neural network

Author:

Cui Xiaoning1ORCID,Wang Qicai12,Dai Jinpeng12,Xue Yanjin12,Duan Yun1

Affiliation:

1. School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou, China

2. Road and Bridge Engineering Disaster Prevention Technology Local Joint National Engineering Laboratory, Lanzhou, China

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

SAGE Publications

Subject

Building and Construction,Civil and Structural Engineering

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