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Remote Sensing
Volume 12
Issue 22
10.3390/rs12223837
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Article

Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach

by
Kyubyung Kang
1,
Donghui Chen
2,
Cheng Peng
2,
Dan Koo
1,
Taewook Kang
3,* and
Jonghoon Kim
4
1
Department of Engineering Technology, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46038, USA
2
Department of Computer and Information Science, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46038, USA
3
Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea
4
Department of Construction Management, University of North Florida, Jacksonville, FL 32224, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3837; https://doi.org/10.3390/rs12223837
Submission received: 29 September 2020 / Revised: 5 November 2020 / Accepted: 13 November 2020 / Published: 23 November 2020
(This article belongs to the Special Issue Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images)
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Abstract

Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.
Keywords: pavement markings; deep learning; visibility; framework pavement markings; deep learning; visibility; framework
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MDPI and ACS Style

Kang, K.; Chen, D.; Peng, C.; Koo, D.; Kang, T.; Kim, J. Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach. Remote Sens. 2020, 12, 3837. https://doi.org/10.3390/rs12223837

AMA Style

Kang K, Chen D, Peng C, Koo D, Kang T, Kim J. Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach. Remote Sensing. 2020; 12(22):3837. https://doi.org/10.3390/rs12223837

Chicago/Turabian Style

Kang, Kyubyung, Donghui Chen, Cheng Peng, Dan Koo, Taewook Kang, and Jonghoon Kim. 2020. "Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach" Remote Sensing 12, no. 22: 3837. https://doi.org/10.3390/rs12223837

APA Style

Kang, K., Chen, D., Peng, C., Koo, D., Kang, T., & Kim, J. (2020). Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach. Remote Sensing, 12(22), 3837. https://doi.org/10.3390/rs12223837

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