Data Augmentation for Improving Deep Learning Models in Building Inspections or Postdisaster Evaluation-Reference-Cited by-同舟云学术

Data Augmentation for Improving Deep Learning Models in Building Inspections or Postdisaster Evaluation

Published:2021-08 Issue:4 Volume:35 Page:
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Leach Samuel¹,Xue Yunhe²,Sridhar Rahul³^ORCID,Paal Stephanie⁴,Wang Zhangyang⁵,Murphy Robin⁶^ORCID

Affiliation:

1. Graduate Research Assistant, Zachry Dept. of Civil and Environmental Engineering, Texas A&M Univ., Engineering Bldg., 201 Dwight Look, College Station, TX 77840 (corresponding author).

2. Graduate Research Assistant, Dept. of Computer Science and Engineering, Texas A&M Univ., H. R. Bright Bldg., 3112 TAMU, 710 Ross St., College Station, TX 77843.

3. Graduate Research Assistant, Dept. of Computer Science and Engineering, Texas A&M Univ., H. R. Bright Bldg., 3112 TAMU, 710 Ross St., College Station, TX 77843. ORCID: .

4. Assistant Professor, Zachry Dept. of Civil and Environmental Engineering, Texas A&M Univ., Engineering Bldg., 201 Dwight Look, College Station, TX 77840.

5. Assistant Professor, Dept. of Electrical and Computer Engineering, Univ. of Texas at Austin, 2501 Speedway, Austin, TX 78712.

6. Raytheon Professor, Dept. of Computer Science and Engineering, Texas A&M Univ., H. R. Bright Bldg., 3112 TAMU, 710 Ross St., College Station, TX 77843. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CF.1943-5509.0001594

Reference78 articles.

1. PCA-Based algorithm for unsupervised bridge crack detection

2. Performance Assessment of Building Infrastructure Impacted by the 2017 Hurricane Harvey in the Port Aransas Region

3. Anantharaman R. M. Velazquez and Y. Lee. 2018. “Utilizing mask R-CNN for detection and segmentation of oral diseases.” In Proc. 2018 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM) 2197–2204. New York: IEEE.

4. Arbeláez P. J. Pont-Tuset J. T. Barron F. Marques and J. Malik. 2014. “Multiscale combinatorial grouping.” In Proc. 2014 IEEE Conf. on Computer Vision and Pattern Recognition 328–335. New York: IEEE. https://doi.org/10.1109/CVPR.2014.49.

5. Arnab A. and P. H. S. Torr. 2017. “Pixelwise instance segmentation with a dynamically instantiated network.” In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 879–888. New York: IEEE. https://doi.org/10.1109/CVPR.2017.100.

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multidamage Identification in High-Resolution Concrete Bridge Component Imagery Based on Deep Learning;Journal of Performance of Constructed Facilities;2024-10

2. Automated UAV image-to-BIM registration for building façade inspection using improved generalised Hough transform;Automation in Construction;2023-09

3. Automatic Quantitative Identification of Bridge Surface Cracks Based on Deep Learning;Journal of Performance of Constructed Facilities;2023-02

4. A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups;Computer-Aided Civil and Infrastructure Engineering;2023-01-05

5. A Taxonomy of Semantic Information in Robot-Assisted Disaster Response;2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR);2022-11-08