Automatic Detection of Pavement Marking Defects in Road Inspection Images Using Deep Learning-Reference-Cited by-同舟云学术

Automatic Detection of Pavement Marking Defects in Road Inspection Images Using Deep Learning

Published:2024-04 Issue:2 Volume:38 Page:
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Sun Yazhen¹,Tang Haixiang²,Zhang Huaizhi³^ORCID

Affiliation:

1. Professor, School of Transportation and Geomatics Engineering, Shenyang Jian Zhu Univ., Shenyang 110168, China.

2. Master’s Candidate, School of Transportation and Geomatics Engineering, Shenyang Jian Zhu Univ., Shenyang 110168, China.

3. Associate Professor, School of Transportation and Geomatics Engineering, Shenyang Jian Zhu Univ., Shenyang 110168, China (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JPCFEV.CFENG-4619

Reference31 articles.

1. Functional Evaluation of Pavement Condition Using a Complete Vision System

2. Bahar, G., M. Masliah, T. Erwin, E. Tan, and E. Hauer. 2006. Pavement marking materials and markers: Real-world relationship between retroreflectivity and safety over time. Thousand Oaks, CA: SAGE.

3. Bochkovskiy A. C.-Y. Wang and H.-Y. M. Liao. 2020. “Yolov4: Optimal speed and accuracy of object detection.” Preprint submitted April 23 2020. http://arxiv.org/abs/2004.10934.

4. Benefits of Pavement Markings

5. Carlson, P. J., G. Schertz, C. Satterfield, K. W. Falk, and T. Taylor. 2014. Methods for maintaining pavement marking retroreflectivity. Washington, DC: Federal Highway Administration, Office of Safety.

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