Transfer-Learning and Texture Features for Recognition of the Conditions of Construction Materials with Small Data Sets-Reference-Cited by-同舟云学术

Transfer-Learning and Texture Features for Recognition of the Conditions of Construction Materials with Small Data Sets

Published:2024-01 Issue:1 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Mengiste Eyob¹^ORCID,Mannem Karunakar Reddy²^ORCID,Prieto Samuel A.³^ORCID,Garcia de Soto Borja⁴^ORCID

Affiliation:

1. S.M.A.R.T. Construction Research Group, Division of Engineering, New York Univ. Abu Dhabi, Abu Dhabi, United Arab Emirates (corresponding author). ORCID: .

2. Center for Research Computing, New York Univ. Abu Dhabi, Abu Dhabi, United Arab Emirates. ORCID: .

3. S.M.A.R.T. Construction Research Group, Division of Engineering, New York Univ. Abu Dhabi, Abu Dhabi, United Arab Emirates. ORCID: .

4. Professor, S.M.A.R.T. Construction Research Group, Division of Engineering, New York Univ. Abu Dhabi, Abu Dhabi, United Arab Emirates. ORCID: .

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-5478

Reference71 articles.

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2. Deep learning in the construction industry: A review of present status and future innovations

3. Augmenting Transfer Learning with Feature Extraction Techniques for Limited Breast Imaging Datasets

4. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

5. Baek F. S. Park and H. Kim. 2019. “Data augmentation using adversarial training for construction-equipment classification.” Preprint submitted February 27 2019. http://arxiv.org/abs/1911.11916.

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