Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks-Reference-Cited by-同舟云学术

Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks

Published:2019-05 Issue:3 Volume:33 Page:04019017
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Park Somin¹,Bang Seongdeok²,Kim Hongjo³,Kim Hyoungkwan⁴

Affiliation:

1. Graduate Student, School of Civil and Environmental Engineering, Yonsei Univ., 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.

2. Ph.D. Candidate, School of Civil and Environmental Engineering, Yonsei Univ., 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea.

3. Postdoctoral Research Fellow, School of Civil and Environmental Engineering, Yonsei Univ., 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea; Research Associate, Dept. of Construction Science, Texas A&M Univ., 400 Bizzell St., College Station, TX 77843.

4. Professor, School of Civil and Environmental Engineering, Yonsei Univ., Seoul 03722, Korea (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000831

Reference38 articles.

1. Abadi M. et al. 2015. “TensorFlow: Large-scale machine learning on heterogeneous systems.” Accessed January 1 2018. https://www.tensorflow.org/.

2. ASCE. 2017. “2017 Infrastructure report card.” Accessed March 20 2017. https://www.infrastructurereportcard.org/.

3. Cost-Effective Preventive Maintenance: Case Studies

4. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

5. NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

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