A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images

Author:

Hou Yue1ORCID,Liu Shuo1,Cao Dandan1ORCID,Peng Bo2ORCID,Liu Zhuo1ORCID,Sun Wenjuan3ORCID,Chen Ning1

Affiliation:

1. Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Chaoyang, Beijing, China

2. Waymo Inc., Market One, San Francisco, CA, USA

3. Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA, USA

Funder

International Research Cooperation Seed Fund of Beijing University of Technology

National Natural Science Foundation of China

Talent Promotion Program by Beijing Association for Science and Technology

Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds (Scientific Research Categories), Beijing

Fundamental Research Funds from BJUT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Reference49 articles.

1. Research on pavement crack recognition based on improved gray scale segmentation algorithm;wang,2014

2. Improved training of Wasserstein GANs;gulrajani;arXiv 1704 00028,2017

3. Learning to compose domain-specific transformations for data augmentation;ratner;Proc Adv Neural Inf Process Syst,2017

4. Improve object detection by data enhancement based on generative adversarial nets;jiang;arXiv 1903 01716,2019

5. Generative adversarial networks

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2. ECSNet: An Accelerated Real-Time Image Segmentation CNN Architecture for Pavement Crack Detection;IEEE Transactions on Intelligent Transportation Systems;2023-12

3. Road Deterioration detection: A Machine Learning-Based System for Automated Pavement Crack Identification and Analysis;2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT);2023-11-20

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