Development of an automated visibility analysis framework for pavement markings based on the deep learning approach
Document Type
Article
Publication Date
11-2-2020
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.
Publication Title
Remote Sensing
Volume
12
Issue
22
First Page
1
Last Page
17
Digital Object Identifier (DOI)
10.3390/rs12223837
E-ISSN
20724292
Citation Information
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), 1-17.