A Deep Learning Model to Evaluate Cracks in the Underground Structure of New Domains-Reference-Cited by-同舟云学术

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A Deep Learning Model to Evaluate Cracks in the Underground Structure of New Domains

Published:2024-02-22 Issue: Volume:67 Page:415-424
ISSN:
Container-title:Geo-Congress 2024
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Author:

Kim Jin¹²¹³⁴,Shim Seungbo¹²¹³⁴,Hwang Hyun-Joong¹²¹³⁴,Seong Joo-Hyun¹²¹³⁴,Cho Gye-Chun¹²¹³⁴

Affiliation:

1. Graduate Student, Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology.

2. Principal Researcher, Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology.

3. Visiting Professor, Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology.

4. Professor, Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485347.042

Reference11 articles.

1. SDDNet: Real-time crack segmentation;Choi W.;IEEE Transactions on Industrial Electronics,2019

2. Farahani A. Voghoei S. Rasheed K. and Arabnia H. R. (2021). “A brief review of domain adaptation.” Advances in data science and information engineering: proceedings from ICDATA 2020 and IKE 2020 877–894.

3. An introduction to ROC analysis

4. Lightweight pixel-wise segmentation for efficient concrete crack detection using hierarchical convolutional neural network;Kim J.;Smart Materials and Structures,2021

5. Kulkarni S. Singh S. Balakrishnan D. Sharma S. Devunuri S. and Korlapati S. C. R. “CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks.” Proc. Computer Vision–ECCV 2022 Workshops: Tel Aviv Israel October 23–27 2022 Proceedings Part VII Springer 179–195.

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