Automated and Contactless Identification of Asphalt Pavement Surface Friction Based on Computer Vision Image-based 3D Reconstruction and Machine Learning Techniques
| 项目名称： | Automated and Contactless Identification of Asphalt Pavement Surface Friction Based on Computer Vision Image-based 3D Reconstruction and Machine Learning Techniques |
| 摘要： | The objective of this research is to develop an automated and contactless 3D scanning and analysis method to identify hot-mix asphalt (HMA) pavement surface friction using close-range photogrammetry. To achieve this objective, a pipeline of (a) computer vision methods for image-based 3D reconstruction and (b) machine learning methods will be used to generate 3D point cloud and mesh models, characterize the geometry and appearance of the pavement surfaces, and infer the observed surface friction. Pavement surface texture will be obtained based on any feed of overlapping images and videos. To streamline the reality capture process, an automatic image acquisition system will be employed. Building on prior work by Ibrahim et al. (2022), a pipeline of image-based 3D reconstruction, multi-view dense reconstruction and mesh modelling will be used to reconstruct the geometry and appearance of the pavement surface texture. Through a user interface, the obtained texture information will be labelled with ground-truth data, and the mapping will be stored in a data set along with the corresponding friction measurements. |
| 状态： | Active |
| 资金： | 750000 |
| 资助组织： | Illinois Department of Transportation |
| 管理组织： | University of Illinois, Urbana-Champaign |
| 主要研究人员： | Al-Qadi, Imad |
| 开始时间： | 20220701 |
| 预计完成日期： | 20260630 |
| 主题领域： | Data and Information Technology；Highways；Materials；Pavements |
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