Automated Bridge Crack Detection Based on Improving Encoder–Decoder Network and Strip Pooling
Publication: Journal of Infrastructure Systems
Volume 29, Issue 2
Abstract
The detection of bridge cracks is an important task in bridge maintenance. It can also reflect the health of the bridge. However, cracks are usually in the form of strips, which are different from the concrete surface. Most crack detection algorithms cannot adapt to this situation well. In this paper, the original image of bridge cracks is collected and the data set is obtained through image processing. A bridge crack detection method based on improving encoder-decoder and mixed pooling module is proposed in this article. The basic features of the crack images are extracted by an encoder with dilated convolution. In this way, the resolution of the feature image can be guaranteed, and large receptive field can be obtained. Then the feature picture through the mix pooling module, which helps to capture remote context information and establish a remote dependency. Finally, the decoder restores the picture to its original size and integrates the original features. In the comparison experiment with the same experimental conditions, we compared with the classic image segmentation methods such as PSPNet, U-Net, FCN, and DeepLabv3+. The results show that our method achieves 98.3%, 97.3%, 97.6%, and 84.5% in precision, recall, F1-score, and MIoU. The results show that our method does have certain advantages in the field of crack detection and segmentation.
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Data Availability Statement
The data images used in this study are all original. All data, models, or codes supporting the results of this study may be obtained from the corresponding author upon reasonable request.
Acknowledgments
The research is jointly supported by the Key Research and Development Program of Shaanxi (2020ZDLGY09-03), the Key Research and Development Program of Guangxi (GK-AB20159032), the Fund of National Engineering and Research Center for Mountainous Highways (GSGZJ-2020-08), the Science and Technology Bureau of Xi’an project (2020KJRC0130), the Open Fund of the Inner Mongolia Transportation Development Research Center (2019KFJJ-006), and the Cooperation project of Technology Department of Guangxi Xinhengtong Expressway (XHT-KXB-2021-006).
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Published In
Journal of Infrastructure Systems
Volume 29 • Issue 2 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 3, 2022
Accepted: Oct 21, 2022
Published online: Jan 17, 2023
Published in print: Jun 1, 2023
Discussion open until: Jun 17, 2023
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