An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks
Automatic road segmentation from three-dimensional point cloud data has gained increasing interest recently. However, it is still challenging to do this task automatically due to the wide variations of roads and complex environments, especially in non-urban areas. This research proposed a comprehens...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10457939/ |
| _version_ | 1797269088611860480 |
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| author | Mohammad Dowajy Arpad Jozsef Somogyi Arpad Barsi Tamas Lovas |
| author_facet | Mohammad Dowajy Arpad Jozsef Somogyi Arpad Barsi Tamas Lovas |
| author_sort | Mohammad Dowajy |
| collection | DOAJ |
| description | Automatic road segmentation from three-dimensional point cloud data has gained increasing interest recently. However, it is still challenging to do this task automatically due to the wide variations of roads and complex environments, especially in non-urban areas. This research proposed a comprehensive approach for using shallow neural networks to segment non-urban road point clouds to support autonomous driving applications. The proposed approach involves converting raw point cloud data into a regular grid of cells or partial clouds. Initially, a shallow neural network based on cells’ properties (cell plane fitting error, cell average intensity, cell elevation range, and cell weighted density) was employed to extract road cells from raw point cloud data. The road cells were refined and segmented into inside-road and road border point clouds based on morphologic operations. A point-wise shallow neural network was used to extract road points from the border point clouds based on intensity and geometric features (roughness, curvature, and change rate of the normal). A precise road surface point cloud is obtained by merging the inside-road and filtered border point clouds. The method performance was evaluated for two datasets captured using a mobile laser scanner (MLS). In the first dataset, the road points were extracted at average completeness, correctness, quality, and overall accuracy of 98.40%, 99.13%, 97.56%, and 98.47%, respectively. Similarly, the method achieved high scores for the second dataset with 97.22% completeness, 99.02% correctness, 96.29% quality, and 98.71% overall accuracy. The method performance demonstrates an advancement when compared to various state-of-the-art methods and also confirms its adaptability to different road environments. |
| first_indexed | 2024-04-25T01:42:49Z |
| format | Article |
| id | doaj.art-1400044109df497f988abd96f5a1ab8c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-25T01:42:49Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1400044109df497f988abd96f5a1ab8c2024-03-08T00:00:29ZengIEEEIEEE Access2169-35362024-01-0112330353304410.1109/ACCESS.2024.337243110457939An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural NetworksMohammad Dowajy0https://orcid.org/0009-0000-2535-9819Arpad Jozsef Somogyi1https://orcid.org/0000-0002-7247-4470Arpad Barsi2https://orcid.org/0000-0002-0298-7502Tamas Lovas3https://orcid.org/0000-0001-6588-6437Department of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Budapest, HungaryDepartment of Photogrammetry and Geoinformatics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Budapest, HungaryAutomatic road segmentation from three-dimensional point cloud data has gained increasing interest recently. However, it is still challenging to do this task automatically due to the wide variations of roads and complex environments, especially in non-urban areas. This research proposed a comprehensive approach for using shallow neural networks to segment non-urban road point clouds to support autonomous driving applications. The proposed approach involves converting raw point cloud data into a regular grid of cells or partial clouds. Initially, a shallow neural network based on cells’ properties (cell plane fitting error, cell average intensity, cell elevation range, and cell weighted density) was employed to extract road cells from raw point cloud data. The road cells were refined and segmented into inside-road and road border point clouds based on morphologic operations. A point-wise shallow neural network was used to extract road points from the border point clouds based on intensity and geometric features (roughness, curvature, and change rate of the normal). A precise road surface point cloud is obtained by merging the inside-road and filtered border point clouds. The method performance was evaluated for two datasets captured using a mobile laser scanner (MLS). In the first dataset, the road points were extracted at average completeness, correctness, quality, and overall accuracy of 98.40%, 99.13%, 97.56%, and 98.47%, respectively. Similarly, the method achieved high scores for the second dataset with 97.22% completeness, 99.02% correctness, 96.29% quality, and 98.71% overall accuracy. The method performance demonstrates an advancement when compared to various state-of-the-art methods and also confirms its adaptability to different road environments.https://ieeexplore.ieee.org/document/10457939/Road segmentation3D point cloudshallow neural networkcell point cloudplan fittingweighted density |
| spellingShingle | Mohammad Dowajy Arpad Jozsef Somogyi Arpad Barsi Tamas Lovas An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks IEEE Access Road segmentation 3D point cloud shallow neural network cell point cloud plan fitting weighted density |
| title | An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks |
| title_full | An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks |
| title_fullStr | An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks |
| title_full_unstemmed | An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks |
| title_short | An Automatic Road Surface Segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks |
| title_sort | automatic road surface segmentation in non urban environments a 3d point cloud approach with grid structure and shallow neural networks |
| topic | Road segmentation 3D point cloud shallow neural network cell point cloud plan fitting weighted density |
| url | https://ieeexplore.ieee.org/document/10457939/ |
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