Structural Crack Detection from Benchmark Data Sets Using Pruned Fully Convolutional Networks-Reference-Cited by-同舟云学术

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Structural Crack Detection from Benchmark Data Sets Using Pruned Fully Convolutional Networks

Published:2021-11 Issue:11 Volume:147 Page:04721008
ISSN:0733-9445
Container-title:Journal of Structural Engineering
language:en
Short-container-title:J. Struct. Eng.

Author:

Ye X. W.¹^ORCID,Jin T.²,Li Z. X.³,Ma S. Y.²,Ding Y.²,Ou Y. H.⁴

Affiliation:

1. Professor, Dept. of Civil Engineering, Zhejiang Univ., Hangzhou 310058, China (corresponding author). ORCID: .

2. Ph.D. Candidate, Dept. of Civil Engineering, Zhejiang Univ., Hangzhou 310058, China.

3. Master’s Candidate, Dept. of Civil Engineering, Zhejiang Univ., Hangzhou 310058, China.

4. B.E. Candidate, Dept. of Civil Engineering, Zhejiang Univ., Hangzhou 310058, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29ST.1943-541X.0003140

Reference62 articles.

1. Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks

2. Encoder–decoder network for pixel‐level road crack detection in black‐box images

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

4. Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

5. Chen L. C. Y. Zhu G. Papandreou F. Schroff and H. Adam. 2018. “Encoder-decoder with Atrous separable convolution for semantic image segmentation.” In Proc. European Conf. on Computer Vision. Berlin: Springer.

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