Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis
Currently, the detection technology for road surface potholes, primarily focuses on the identification and segmentation, lacking the ability to quantitatively analyze the damage inflicted by road potholes. Therefore, this pa per proposes a method based on three-dimensional point clouds for the ident...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10845756/ |
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| author | Qingzhen Sun Lei Qiao Yibo Shen |
| author_facet | Qingzhen Sun Lei Qiao Yibo Shen |
| author_sort | Qingzhen Sun |
| collection | DOAJ |
| description | Currently, the detection technology for road surface potholes, primarily focuses on the identification and segmentation, lacking the ability to quantitatively analyze the damage inflicted by road potholes. Therefore, this pa per proposes a method based on three-dimensional point clouds for the identification, segmentation, and reconstruction of road potholes, ultimately leading to the quantification of the damage volume. An RGB-D depth sensor is employed to collect point cloud data of road potholes. Voxel filtering and voxelization downsampling are used for denoising, filtering, and enhancing data processing efficiency. Surface segmentation is achieved through RANSAC (Random Sample Consensus) and Euclidean clustering, while the Alpha Shapes algorithm is utilized for three-dimensional volume reconstruction, facilitating the volumetric quantification of potholes. For evaluation, comparative experiments were conducted under different lighting conditions and shooting distances. The experimental results demonstrate that the proposed algorithm achieves an accuracy of 96.4% in volumetric damage measurement of road potholes, accurately determining the damage volume of pothole. |
| format | Article |
| id | doaj-art-02fa8881efb64ecf99911f66e345d001 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-02fa8881efb64ecf99911f66e345d0012025-01-28T00:01:10ZengIEEEIEEE Access2169-35362025-01-0113129451295510.1109/ACCESS.2025.353176610845756Pavement Potholes Quantification: A Study Based on 3D Point Cloud AnalysisQingzhen Sun0Lei Qiao1https://orcid.org/0009-0006-1508-3189Yibo Shen2https://orcid.org/0009-0005-9989-8336School of Civil Engineering and Environment, Zhengzhou University of Aeronautics, Zhengzhou, Henan, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, ChinaSchool of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, ChinaCurrently, the detection technology for road surface potholes, primarily focuses on the identification and segmentation, lacking the ability to quantitatively analyze the damage inflicted by road potholes. Therefore, this pa per proposes a method based on three-dimensional point clouds for the identification, segmentation, and reconstruction of road potholes, ultimately leading to the quantification of the damage volume. An RGB-D depth sensor is employed to collect point cloud data of road potholes. Voxel filtering and voxelization downsampling are used for denoising, filtering, and enhancing data processing efficiency. Surface segmentation is achieved through RANSAC (Random Sample Consensus) and Euclidean clustering, while the Alpha Shapes algorithm is utilized for three-dimensional volume reconstruction, facilitating the volumetric quantification of potholes. For evaluation, comparative experiments were conducted under different lighting conditions and shooting distances. The experimental results demonstrate that the proposed algorithm achieves an accuracy of 96.4% in volumetric damage measurement of road potholes, accurately determining the damage volume of pothole.https://ieeexplore.ieee.org/document/10845756/Potholesthree-dimensional point clouddepth sensing camerasurface segmentation |
| spellingShingle | Qingzhen Sun Lei Qiao Yibo Shen Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis IEEE Access Potholes three-dimensional point cloud depth sensing camera surface segmentation |
| title | Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis |
| title_full | Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis |
| title_fullStr | Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis |
| title_full_unstemmed | Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis |
| title_short | Pavement Potholes Quantification: A Study Based on 3D Point Cloud Analysis |
| title_sort | pavement potholes quantification a study based on 3d point cloud analysis |
| topic | Potholes three-dimensional point cloud depth sensing camera surface segmentation |
| url | https://ieeexplore.ieee.org/document/10845756/ |
| work_keys_str_mv | AT qingzhensun pavementpotholesquantificationastudybasedon3dpointcloudanalysis AT leiqiao pavementpotholesquantificationastudybasedon3dpointcloudanalysis AT yiboshen pavementpotholesquantificationastudybasedon3dpointcloudanalysis |