Deep Learning Enhanced Crack Detection for Tunnel Inspection
Author:
Affiliation:
1. Dept. of Civil and Environmental Engineering, Kennesaw State Univ., Marietta, GA.
2. School of Civil Engineering, Chongqing Jiaotong Univ., Chongqing, China.
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784485514.064
Reference18 articles.
1. Correlation Among the Soil Parameters of the Karnaphuli River Tunnel Project;Ahmad K. I.;GEOMATE Journal,2018
2. Belloni V. A. Sjölander R. Ravanelli M. Crespi and A. Nascetti. 2020. “Tack Project: Tunnel and Bridge Automatic Crack Monitoring Using Deep Learning and Photogrammetry.” The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences XLIII-B4-2020: 741–745. https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-741-2020.
3. Development of Extendable Open-Source Structural Inspection Datasets
4. Automatic tunnel lining crack evaluation and measurement using deep learning
5. Measurement of Crack Width Using Digital Photogrammetry for Evaluation of the Stability of Tunnel;Hirota A.;Journal of Japan Society of Civil Engineers, Ser. F1 (Tunnel Engineering),2016
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