Deep Learning-Based Three-dimensional Reconstruction and Spatial Position Mapping of Tunnel Defect Zones-Reference-Cited by-同舟云学术

Deep Learning-Based Three-dimensional Reconstruction and Spatial Position Mapping of Tunnel Defect Zones

Published:2024-05-17 Issue: Volume:21 Page:118-123
ISSN:
Container-title:Proceedings of the 2024 3rd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence
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Author:

Wang Jing¹^ORCID,Wang Wenying¹^ORCID,Xu Ping²^ORCID,Wang Chuan³^ORCID,Zhang Ning³^ORCID,Gao Sheng¹^ORCID,Xiao Ganghua¹^ORCID,Wang Zhengfang¹^ORCID

Affiliation:

1. School of Control science and engineering, Shandong University, China

2. Shandong Jiekong Electric Technology Co., Ltd, China

3. Shandong Hi-speed Group Co., Ltd and Shandong University, China

Funder

Shandong Province Science and Technology Innovation Capacity Enhancement Project for Small and Medium-sized Enterprises

Key R&D Program of Shandong Province

National Natural Science Foundation of China

Young Innovation Team Program of Shandong Higher Education Institution

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3669721.3671009

Reference14 articles.

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2. Meng Y, Wu H, Niu B. A Method to Improve the Lining Images Quality in Complex Tunnel Scenes[C]. 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2020: 199-203.

3. Niu B, Wu H, Meng Y. Application of CEM Algorithm in the Field of Tunnel Crack Identification[C]. 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2020: 232-236.

4. Sealed-Crack Detection Algorithm Using Heuristic Thresholding Approach

5. Linear seam elimination of tunnel crack images based on statistical specific pixels ratio and adaptive fragmented segmentation[J];Qu Z;IEEE Transactions on Intelligent Transportation Systems,2019