Using Computer Vision and Deep Learning Techniques to Extract Roadway Geometry from Aerial Images
| 项目名称： | Using Computer Vision and Deep Learning Techniques to Extract Roadway Geometry from Aerial Images |
| 摘要： | The overall goal of this project is to develop computer vision tools to extract different roadway geometry data such as school zone markings, lane configurations (i.e., turning lanes lengths, and lane, shoulder and median widths), presence of signals (i.e., identification of signal poles), and sidewalks (i.e., presence or absence of sidewalks) from high resolution aerial images, which can be used by FDOT planners and engineers at various levels of traffic operations and safety analysis. Consistent with this goal, the main objectives of this project are to: (a) examine how traffic data collection can leverage emerging computer vision techniques, in particular, image processing, deep learning, machine learning, and artificial intelligence to develop statewide roadway inventory lists; (b) design an automated signalized intersection geometric data extraction algorithm based on high-resolution images in order to identify roadway geometry data such as school zone markings, lane configurations (i.e., turning lanes and their lengths), and sidewalks (i.e., presence or absence of sidewalks) from high resolution aerial images, and (c) generate a geographic information services (GIS)-based inventory list of these roadway geometry features for the entire state of Florida including ON and OFF roadways. This is an innovative solution that employs the computer vision technology to potentially replace traditional manual inventory, which is labor intensive and prone to errors. |
| 状态： | Active |
| 资金： | 223843 |
| 资助组织： | Florida Department of Transportation |
| 管理组织： | Department of Transpotation |
| 项目负责人： | EI-Urfali, Alan |
| 执行机构： | Florida State University, Tallahassee |
| 主要研究人员： | Ozguven, Eren |
| 开始时间： | 20220330 |
| 预计完成日期： | 20230831 |
| 主题领域： | Data and Information Technology；Highways |
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