Unsupervised Domain Adaptation Approach for Vision-Based Semantic Understanding of Bridge Inspection Scenes without Manual Annotations-Reference-Cited by-同舟云学术

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Unsupervised Domain Adaptation Approach for Vision-Based Semantic Understanding of Bridge Inspection Scenes without Manual Annotations

Published:2024-02 Issue:2 Volume:29 Page:
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Narazaki Yasutaka¹^ORCID,Pang Wendong²,Wang Gaoang³,Chai Wenhao⁴

Affiliation:

1. Assistant Professor, Zhejiang Univ./Univ. of Illinois Urbana-Champaign Institute, Haining, Zhejiang 314400, China (corresponding author). ORCID: .

2. Graduate Student, Zhejiang Univ./Univ. of Illinois Urbana-Champaign Institute, Haining, Zhejiang 314400, China.

3. Assistant Professor, Zhejiang Univ./Univ. of Illinois Urbana-Champaign Institute, Haining, Zhejiang 314400, China.

4. Graduate Student, Dept. of Electrical and Computer Engineering, Univ. of Washington, Seattle, WA 98195.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JBENF2.BEENG-6490

Reference58 articles.

1. Machine learning paradigm for structural health monitoring

2. Bentley. n.d. “Bridge design 1 software|Bentley systems.” Accessed August 9 2023. https://www.bentley.com/software/bridge-design-1/.

3. Visual structural inspection datasets

4. Chang A. X. 2015. “ShapeNet: An information-rich 3D model repository.” Preprint submitted December 9 2015. https://arxiv.org/abs/1512.03012v1.

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

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