能见度
计算机科学
运输工程
人工智能
工程类
地理
气象学
作者
Kyubyung Kang,Donghui Chen,Cheng Peng,Dan Koo,Tae Wook Kang,Jonghoon Kim
出处
期刊:Remote Sensing
[MDPI AG]
日期:2020-11-23
卷期号:12 (22): 3837-3837
被引量:11
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI