Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network-Reference-Cited by-同舟云学术

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Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network

Published:2019-11 Issue: Volume:107 Page:102946
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Huyan Ju,Li Wei,Tighe Susan,Zhai Junzhi,Xu Zhengchao,Chen Yao

Funder

Nature Science Program of Shaanxi Province

Fundamental Research Funds for the Central Universities

University of Waterloo

Norman W. McLeod Chair in Sustainable Pavement Engineering at the University of Waterloo

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference46 articles.

1. Multiple crack detection in 3D using a stable XFEM and global optimization;Agathos;Comput. Mech.,2018

2. A multi-stage expert system for classification of pavement cracking;Zakeri,2013

3. A fast and accurate automated pavement crack detection algorithm;Chatterjee,2018

4. Efficient pavement crack detection and classification;Cubero-Fernandez;Eurasip J. Image Video Process.,2017

5. Sustainable and Resilient Critical Infrastructure Systems: Simulation, Modeling, and Intelligent Engineering;Gopalakrishnan,2010

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