A GAN-Augmented CNN Approach for Automated Roadside Safety Assessment of Rural Roadways-Reference-Cited by-同舟云学术

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A GAN-Augmented CNN Approach for Automated Roadside Safety Assessment of Rural Roadways

Published:2024-03 Issue:2 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Hassandokht Mashhadi Ali¹^ORCID,Rashidi Abbas²,Marković Nikola³

Affiliation:

1. Ph.D. Student, Dept. of Civil and Environmental Engineering, Univ. of Utah, Salt Lake City, UT 84112 (corresponding author). ORCID: .

2. Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Utah, Salt Lake City, UT 84112.

3. Assistant Professor, Dept. of Civil and Environmental Engineering, Univ. of Utah, Salt Lake City, UT 84112.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5406

Reference32 articles.

1. Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

2. 3D Photogrammetry Point Cloud Segmentation Using a Model Ensembling Framework

3. A design method for the roadside clear zone based on accident simulation analysis;Cheng R.;Math. Probl. Eng.,2021

4. Dai F. A. Rashidi I. Brilakis and P. Vela. 2012. “Comparison of image-based and time-of-flight-based technologies for three-dimensional reconstruction of infrastructure.” In Proc. Construction Research Congress 929–940. Reston VA: ASCE.

5. Deng J. W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. “Imagenet: A large-scale hierarchical image database.” In Proc. IEEE Conf. on Computer Vision and Pattern Recognition 248–255. New York: IEEE.

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