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

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....

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-11, Vol.12 (22), p.3837
Main Authors: Kang, Kyubyung, Chen, Donghui, Peng, Cheng, Koo, Dan, Kang, Taewook, Kim, Jonghoon
Format: Article
Language:English
Subjects:
Accuracy
Asphalt pavements
Automation
Classification
Cracks
Crashes
Data processing
Datasets
Deep learning
framework
Human resources
Learning algorithms
Machine learning
Modules
Neural networks
Pavement markings
Pavements
Principal components analysis
Road maintenance
Roads & highways
Safety
Vehicle safety
Visibility
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12223837