Automatic Pavement Type Recognition for Image-Based Pavement Condition Survey Using Convolutional Neural Network-Reference-Cited by-同舟云学术

Automatic Pavement Type Recognition for Image-Based Pavement Condition Survey Using Convolutional Neural Network

Published:2021-01 Issue:1 Volume:35 Page:04020060
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Yang Guangwei¹^ORCID,Wang Kelvin C. P.²,Li Joshua Qiang³,Fei Yue⁴,Liu Yang⁵,Mahboub Kamyar C.⁶^ORCID,Zhang Allen A.⁷^ORCID

Affiliation:

1. Postdoctoral Researcher, School of Civil and Environmental Engineering, Oklahoma State Univ., Stillwater, OK 74078. ORCID:

2. Regents Professor, School of Civil and Environmental Engineering, Oklahoma State Univ., Stillwater, OK 74078 (corresponding author).

3. Associate Professor, School of Civil and Environmental Engineering, Oklahoma State Univ., Stillwater, OK 74078.

4. Algorithm Engineer, Chengdu Guimu Robot, 888 Chenglong Rd., Chengdu, Sichuan 610000, China.

5. Postdoctoral Researcher, School of Civil and Environmental Engineering, Oklahoma State Univ., Stillwater, OK 74078.

6. Professor, Dept. of Civil Engineering, Univ. of Kentucky, Lexington, KY 40503. ORCID:

7. Senior Researcher, Guangdong Provincial Academy of Building Research, 121 E. Xianlie Rd., Guangzhou 510500, China. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000944

Reference27 articles.

1. ARTBA (American Road & Transportation Builders Association). 2020. “Frequently asked questions.” Accessed January 19 2020. https://www.artba.org/about/faq/.

2. Bystrov A. M. Abbas E. Hoare T. Tran N. Clarke M. Gashinova and M. Cherniakov. 2015. “Analysis of classification algorithms applied to road surface recognition.” In Proc. 2015 IEEE Radar Conference (RadarCon) 907–911. New York: IEEE. https://doi.org/10.1109/RADAR.2015.7131124.

3. Automatic road distress detection and analysis;Daniel A.;Int. J. Comput. Appl.,2014

4. Faghih-Roohi S. S. Hajizadeh A. Núñez R. Babuska and B. De Schutter. 2016. “Deep convolutional neural networks for detection of rail surface defects.” In Proc. 2016 Int. Joint Conf. on Neural Networks (IJCNN) 2584–2589. New York: IEEE. https://doi.org/10.1109/IJCNN.2016.7727522.

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A New Pavement Recognition Method of 24-GHz Radar Based on Prior Knowledge and Data-Driven;IEEE Sensors Journal;2024-03-15

2. A lightweight convolutional neural network for automated crack inspection;Construction and Building Materials;2024-02

3. Road friction estimation based on vision for safe autonomous driving;Mechanical Systems and Signal Processing;2024-02

4. A Study on the Application of Image Analysis Techniques to Autocoders in Civil and Hydraulic Engineering;Applied Mathematics and Nonlinear Sciences;2024-01-01

5. Methodology for Selecting Neural Network Architecture for Recognizing Pavement Defects;2023 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED);2023-11-15