Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network
Author:
Affiliation:
1. School of Civil and Environmental Engineering; Oklahoma State University; OK USA
2. School of Civil Engineering; Southwest Jiaotong University; Chengdu China
3. School of Transportation Engineering; Beijing University of Technology; China
Funder
U.S. Federal Aviation Administration
National Natural Science Foundation of China
Publisher
Wiley
Subject
Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering
Link
http://onlinelibrary.wiley.com/wol1/doi/10.1111/mice.12409/fullpdf
Reference42 articles.
1. Neuro-fuzzy logic model for freeway work zone capacity estimation;Adeli;Journal of Transportation Engineering,2003
2. An adaptive conjugate gradient neural network-wavelet model for traffic incident detection;Adeli;Computer-Aided Civil and Infrastructure Engineering,2000
3. Deep learning-based crack damage detection using convolutional neural networks;Cha;Computer-Aided Civil and Infrastructure Engineering,2017a
4. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types;Cha;Computer-Aided Civil and Infrastructure Engineering,2017b
5. On the properties of neural machine translation: encoder-decoder approaches;Cho;arXiv,2014
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