Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network

Author:

Zhang Allen1,Wang Kelvin C. P.12,Fei Yue1,Liu Yang1,Chen Cheng1,Yang Guangwei1,Li Joshua Q.1,Yang Enhui2,Qiu Shi3

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

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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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