Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images
Accurate road marking extraction is essential for advancing digital transportation systems, autonomous vehicles, and high-definition maps. Although existing methods focus on extracting high-precision road markings from Mobile Laser Scanning (MLS) point clouds, they still face challenges in practical...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2531842 |
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| _version_ | 1849224317645094912 |
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| author | Dehui Li Tao Liu Ping Du Tianen Ma Shuangtong Liu |
| author_facet | Dehui Li Tao Liu Ping Du Tianen Ma Shuangtong Liu |
| author_sort | Dehui Li |
| collection | DOAJ |
| description | Accurate road marking extraction is essential for advancing digital transportation systems, autonomous vehicles, and high-definition maps. Although existing methods focus on extracting high-precision road markings from Mobile Laser Scanning (MLS) point clouds, they still face challenges in practical applications, including indistinguishable instances, category ambiguity, and incomplete boundary segmentation, which collectively limit their overall performance. To address these challenges, we convert MLS point clouds into intensity images and propose a deep learning model that integrates context modelling and boundary refinement for accurate instance segmentation of road markings. First, a feature enhancement module (FEM) is designed to improve road marking representation by learning dependencies across channel and spatial dimensions. Second, a multiscale context-aware module (MSCAM) is constructed to enhance the model's capacity to identify diverse marking types by aggregating semantic information from multi-scale distant regions. Lastly, a PointRend module (PRM) is introduced to adaptively select key points for prediction, generating high-quality boundary masks. Experiments on a newly constructed dataset reveal that, compared with state-of-the-art instance segmentation models, our method offers substantial performance advantages. The model accurately detects and segments eight categories of road markings, achieving 77.1% APb and 59.8% APm on Test Set 1. |
| format | Article |
| id | doaj-art-b4d15ac64b1f45a1be3b717747dc83a5 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-b4d15ac64b1f45a1be3b717747dc83a52025-08-25T11:31:54ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2531842Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity imagesDehui Li0Tao Liu1Ping Du2Tianen Ma3Shuangtong Liu4Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, People’s Republic of ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, People’s Republic of ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, People’s Republic of ChinaSchool of Marine Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, People’s Republic of ChinaAccurate road marking extraction is essential for advancing digital transportation systems, autonomous vehicles, and high-definition maps. Although existing methods focus on extracting high-precision road markings from Mobile Laser Scanning (MLS) point clouds, they still face challenges in practical applications, including indistinguishable instances, category ambiguity, and incomplete boundary segmentation, which collectively limit their overall performance. To address these challenges, we convert MLS point clouds into intensity images and propose a deep learning model that integrates context modelling and boundary refinement for accurate instance segmentation of road markings. First, a feature enhancement module (FEM) is designed to improve road marking representation by learning dependencies across channel and spatial dimensions. Second, a multiscale context-aware module (MSCAM) is constructed to enhance the model's capacity to identify diverse marking types by aggregating semantic information from multi-scale distant regions. Lastly, a PointRend module (PRM) is introduced to adaptively select key points for prediction, generating high-quality boundary masks. Experiments on a newly constructed dataset reveal that, compared with state-of-the-art instance segmentation models, our method offers substantial performance advantages. The model accurately detects and segments eight categories of road markings, achieving 77.1% APb and 59.8% APm on Test Set 1.https://www.tandfonline.com/doi/10.1080/17538947.2025.2531842Road markingsinstance segmentationmobile laser scanning (MLS) point cloudsboundary optimizedcontext-aware |
| spellingShingle | Dehui Li Tao Liu Ping Du Tianen Ma Shuangtong Liu Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images International Journal of Digital Earth Road markings instance segmentation mobile laser scanning (MLS) point clouds boundary optimized context-aware |
| title | Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images |
| title_full | Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images |
| title_fullStr | Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images |
| title_full_unstemmed | Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images |
| title_short | Context-aware and boundary-optimized model for road marking instance segmentation using MLS point cloud intensity images |
| title_sort | context aware and boundary optimized model for road marking instance segmentation using mls point cloud intensity images |
| topic | Road markings instance segmentation mobile laser scanning (MLS) point clouds boundary optimized context-aware |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2531842 |
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