Recognition of asphalt pavement crack length using deep convolutional neural networks-Reference-Cited by-同舟云学术

Recognition of asphalt pavement crack length using deep convolutional neural networks

Published:2017-04-04 Issue:6 Volume:19 Page:1334-1349
ISSN:1468-0629
Container-title:Road Materials and Pavement Design
language:en
Short-container-title:Road Materials and Pavement Design

Author:

Tong Zheng¹,Gao Jie¹²,Han Zhenqiang¹,Wang Zhenjun³

Affiliation:

1. School of Highway, Chang’an University, Xi’an 710061, People’s Republic of China

2. National and Local Joint Engineering Materials Laboratory of Traffic Engineering and Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, People’s Republic of China

3. School of Materials Science and Engineering, Chang’an University, Xi’an 710061, People’s Republic of China

Funder

Fundamental Research Funds for the Central Universities of China

Chongqing Jiaotong University

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/14680629.2017.1308265

Reference32 articles.

1. String representations and distances in deep Convolutional Neural Networks for image classification

2. Representation Learning: A Review and New Perspectives

3. Three-dimensional finite element modelling of all-ceramic restorations based on micro-CT

4. Automated pavement distress data collection and analysis: a 3-D approach

5. Scalability of Stochastic Gradient Descent based on “Smart” Sampling Techniques

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