地下空间（英文）2025，Vol.22Issue(3)：96-109,14.DOI:10.1016/j.undsp.2024.10.002
High-precision segmentation and quantification of tunnel lining crack using an improved DeepLabV3+
High-precision segmentation and quantification of tunnel lining crack using an improved DeepLabV3+
Zhutian Pan 1Xuepeng Zhang 2Yujing Jiang 3Bo Li 4Naser Golsanami 2Hang Su 5Yue Cai6
作者信息
- 1. State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,Shandong University of Science and Technology,Qingdao 266590,China||College of Safety and Environmental Engineering,Shandong University of Science and Technology,Qingdao 266590,China
- 2. State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,Shandong University of Science and Technology,Qingdao 266590,China||College of Energy and Mining Engineering,Shandong University of Science and Technology,Qingdao 266590,China
- 3. State Key Laboratory of Strata Intelligent Control and Green Mining Co-founded by Shandong Province and the Ministry of Science and Technology,Shandong University of Science and Technology,Qingdao 266590,China||School of Engineering,Nagasaki University,Nagasaki 8528521,Japan
- 4. College of Civil Engineering,Tongji University,Shanghai 200092,China
- 5. College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China
- 6. Fukumichi Limited Liability Company,Fukuoka 813-0041,Japan
- 折叠
摘要
关键词
Tunnel lining/Deep learning/Surface crack/DeepLabV3+Key words
Tunnel lining/Deep learning/Surface crack/DeepLabV3+引用本文复制引用
Zhutian Pan,Xuepeng Zhang,Yujing Jiang,Bo Li,Naser Golsanami,Hang Su,Yue Cai..High-precision segmentation and quantification of tunnel lining crack using an improved DeepLabV3+[J].地下空间（英文）,2025,22(3):96-109,14.基金项目
This work was funded by the National Natural Science Foundation of China(Grant No.52109132),and Shan-dong Provincial Natural Science Foundation(Grant No.ZR2020QE270). （Grant No.52109132）
