Crack Detection via Hierarchical Multiscale Feature Learning and Densely Connected Conditional Random Field-Reference-Cited by-同舟云学术

Crack Detection via Hierarchical Multiscale Feature Learning and Densely Connected Conditional Random Field

Published:2024-03 Issue:1 Volume:10 Page:
ISSN:2376-7642
Container-title:ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
language:en
Short-container-title:ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

Author:

Chen Liaogehao¹^ORCID,Zhu Huaguang²,Li Jiali³,Liang Chaojie²,Zhang Zhenjun⁴,Wang Yaonan⁵

Affiliation:

1. Ph.D. Candidate, College of Electrical and Information Engineering, Hunan Univ., Changsha 410082, China. ORCID: .

2. Senior Engineer, Dept. of System Design, Beijing Institute of Astronautical Systems Engineering, Beijing 100000, China.

3. Master’s Candidate, College of Electrical and Information Engineering, Hunan Univ., Changsha 410082, China.

4. Associate Professor, College of Electrical and Information Engineering, Hunan Univ., Changsha 410082, China (corresponding author).

5. Professor, College of Electrical and Information Engineering, Hunan Univ., Changsha 410082, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/AJRUA6.RUENG-1102

Reference32 articles.

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2. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

3. Cogswell M. X. Lin S. Purushwalkam and D. Batra. 2014. “Combining the best of graphical models and convnets for semantic segmentation.” Preprint submitted December 14 2014. http://arxiv.org/abs/1412.4313.

4. A Data-Driven Physics-Informed Method for Prognosis of Infrastructure Systems: Theory and Application to Crack Prediction

5. Review on computer vision-based crack detection and quantification methodologies for civil structures