Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention
Accurate detection of road markings is critically important in fields such as autonomous driving technology, high-precision mapping, and intelligent transportation systems. Unlike traditional object detection tasks, road marking detection faces many challenges, including significant direction change...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10985801/ |
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| author | Dehui Li Tao Liu Ping Du Yi He Shuangtong Liu |
| author_facet | Dehui Li Tao Liu Ping Du Yi He Shuangtong Liu |
| author_sort | Dehui Li |
| collection | DOAJ |
| description | Accurate detection of road markings is critically important in fields such as autonomous driving technology, high-precision mapping, and intelligent transportation systems. Unlike traditional object detection tasks, road marking detection faces many challenges, including significant direction changes, complex backgrounds, and diverse types of road markings. To address these issues, this article proposes a deep learning approach based on mobile laser scanning point cloud intensity images to accurately detect road markings with arbitrary orientations. First, we employ an oriented bounding box to precisely represent the direction and location of road markings, thereby improving detection accuracy. Additionally, the channel semantic enhanced module (SEM) is designed to enhance the feature representation capacity of the backbone network, effectively reducing the impact of complex backgrounds on foreground targets. Finally, the spatial SEM is introduced to enhance the feature pyramid network's ability to capture contextual information. The module consists of the large selective kernel network (LSKNet) and the SEM. LSKNet extracts contextual information around the target by adaptively selecting receptive fields of various scales, while SEM, a lightweight semantic segmentation branch, further strengthens the feature representation ability of this information by incorporating a semantic supervision mechanism. The proposed method achieves an mAP of 84.7% on Test Set I, representing a 3.2% improvement over the baseline model, while maintaining high efficiency, offering a robust and reliable solution for real-time road marking detection tasks. |
| format | Article |
| id | doaj-art-bc7e97ef656647b38452a3babe0ff858 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-bc7e97ef656647b38452a3babe0ff8582025-08-20T03:13:42ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118125881260210.1109/JSTARS.2025.356699810985801Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial AttentionDehui Li0https://orcid.org/0009-0005-3459-9765Tao Liu1https://orcid.org/0000-0003-0202-0032Ping Du2Yi He3https://orcid.org/0000-0003-4017-0488Shuangtong Liu4https://orcid.org/0009-0004-6183-8680Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, Gansu, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, Gansu, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, Gansu, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, Gansu, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, Gansu, ChinaAccurate detection of road markings is critically important in fields such as autonomous driving technology, high-precision mapping, and intelligent transportation systems. Unlike traditional object detection tasks, road marking detection faces many challenges, including significant direction changes, complex backgrounds, and diverse types of road markings. To address these issues, this article proposes a deep learning approach based on mobile laser scanning point cloud intensity images to accurately detect road markings with arbitrary orientations. First, we employ an oriented bounding box to precisely represent the direction and location of road markings, thereby improving detection accuracy. Additionally, the channel semantic enhanced module (SEM) is designed to enhance the feature representation capacity of the backbone network, effectively reducing the impact of complex backgrounds on foreground targets. Finally, the spatial SEM is introduced to enhance the feature pyramid network's ability to capture contextual information. The module consists of the large selective kernel network (LSKNet) and the SEM. LSKNet extracts contextual information around the target by adaptively selecting receptive fields of various scales, while SEM, a lightweight semantic segmentation branch, further strengthens the feature representation ability of this information by incorporating a semantic supervision mechanism. The proposed method achieves an mAP of 84.7% on Test Set I, representing a 3.2% improvement over the baseline model, while maintaining high efficiency, offering a robust and reliable solution for real-time road marking detection tasks.https://ieeexplore.ieee.org/document/10985801/Channel and spatial attentionmobile laser scanning (MLS) point cloudsoriented road markingssemantic enhancement |
| spellingShingle | Dehui Li Tao Liu Ping Du Yi He Shuangtong Liu Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Channel and spatial attention mobile laser scanning (MLS) point clouds oriented road markings semantic enhancement |
| title | Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention |
| title_full | Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention |
| title_fullStr | Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention |
| title_full_unstemmed | Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention |
| title_short | Oriented Road Marking Detection in MLS Point Cloud Intensity Images Using Channel and Spatial Attention |
| title_sort | oriented road marking detection in mls point cloud intensity images using channel and spatial attention |
| topic | Channel and spatial attention mobile laser scanning (MLS) point clouds oriented road markings semantic enhancement |
| url | https://ieeexplore.ieee.org/document/10985801/ |
| work_keys_str_mv | AT dehuili orientedroadmarkingdetectioninmlspointcloudintensityimagesusingchannelandspatialattention AT taoliu orientedroadmarkingdetectioninmlspointcloudintensityimagesusingchannelandspatialattention AT pingdu orientedroadmarkingdetectioninmlspointcloudintensityimagesusingchannelandspatialattention AT yihe orientedroadmarkingdetectioninmlspointcloudintensityimagesusingchannelandspatialattention AT shuangtongliu orientedroadmarkingdetectioninmlspointcloudintensityimagesusingchannelandspatialattention |