Lane Line Semantic Segmentation, Modeling and Road Region Detection Based on UAV Edge Computing.
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| Title: | Lane Line Semantic Segmentation, Modeling and Road Region Detection Based on UAV Edge Computing. |
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| Authors: | Wang, Yuehao1 (AUTHOR), Liu, Haiqing1,2 (AUTHOR) hqliu@sdjtu.edu.cn, Zhang, Mengmeng1,3 (AUTHOR), Yu, Lei1,2 (AUTHOR), Ma, Dongfang2,3 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1820. 22p. |
| Subjects: | Edge computing, Road markings, Traffic monitoring, Road construction, Intelligent transportation systems, Artificial neural networks |
| Abstract: | Highlights: What are the main findings? This study proposes a UAV-driven edge-based lane-detection framework and a lightweight lane line semantic segmentation model, LSLNet; this integrates the strip-aware multi-branch depthwise operator (SMDO) and the Sobel-based feature-fusion scheme (SFFS) to enhance lane line extraction under limited onboard computing resources. On the UAV-Laneline3K dataset, LSLNet achieves 82.73% F1-score and 72.06% mIoU at 82 FPS with only 0.09M parameters for lane line semantic segmentation, while the proposed road-region-detection method attains 97.62% mIoU on the UAV-Roadregion200 dataset. What is the implication of the main finding? The proposed framework provides an effective solution for real-time lane-level road perception and road region detection on UAV edge platforms, which is beneficial for traffic monitoring, incident analysis, and intelligent transportation applications. The lightweight network design and structured lane modeling strategy can also be extended to other UAV-based semantic segmentation and geometric reconstruction tasks in resource-constrained scenarios. UAV-based road traffic state monitoring and analysis have become a hotspot in current research, where road region detection serves as the prerequisite for the aforementioned applications. This paper proposes a UAV-driven edge-based lane-detection system, and a lane line semantic segmentation, modeling and road-region-detection method. Firstly, a lightweight lane line semantic segmentation model LSLNet is presented, where the strip- aware multi-branch depthwise operator (SMDO) and the Sobel-based feature-fusion scheme (SFFS) are used in conjunction to improve feature representation ability under low computational overheads. Furthermore, the segmented lane line mask is quantified into a parametric form and the lane-level road regions are constructed by lane line spatial geometric distribution. Finally, to evaluate the performance of the proposed method, an experiment is conducted using the self-constructed UAV-Laneline3K and UAV-Roadregion200 datasets. The experimental results show that LSLNet achieves 82.73% F1-score and 72.06% mIoU on the lane line semantic segmentation task, which runs at 82 FPS with merely 0.09M parameters and 13.0 GFLOPs. For road region detection, the mIoU and F1-score reach 97.62% and 98.86%, respectively. The results demonstrate that the proposed method enables accurate and robust road region detection in complex road environments with low computational costs. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? This study proposes a UAV-driven edge-based lane-detection framework and a lightweight lane line semantic segmentation model, LSLNet; this integrates the strip-aware multi-branch depthwise operator (SMDO) and the Sobel-based feature-fusion scheme (SFFS) to enhance lane line extraction under limited onboard computing resources. On the UAV-Laneline3K dataset, LSLNet achieves 82.73% F1-score and 72.06% mIoU at 82 FPS with only 0.09M parameters for lane line semantic segmentation, while the proposed road-region-detection method attains 97.62% mIoU on the UAV-Roadregion200 dataset. What is the implication of the main finding? The proposed framework provides an effective solution for real-time lane-level road perception and road region detection on UAV edge platforms, which is beneficial for traffic monitoring, incident analysis, and intelligent transportation applications. The lightweight network design and structured lane modeling strategy can also be extended to other UAV-based semantic segmentation and geometric reconstruction tasks in resource-constrained scenarios. UAV-based road traffic state monitoring and analysis have become a hotspot in current research, where road region detection serves as the prerequisite for the aforementioned applications. This paper proposes a UAV-driven edge-based lane-detection system, and a lane line semantic segmentation, modeling and road-region-detection method. Firstly, a lightweight lane line semantic segmentation model LSLNet is presented, where the strip- aware multi-branch depthwise operator (SMDO) and the Sobel-based feature-fusion scheme (SFFS) are used in conjunction to improve feature representation ability under low computational overheads. Furthermore, the segmented lane line mask is quantified into a parametric form and the lane-level road regions are constructed by lane line spatial geometric distribution. Finally, to evaluate the performance of the proposed method, an experiment is conducted using the self-constructed UAV-Laneline3K and UAV-Roadregion200 datasets. The experimental results show that LSLNet achieves 82.73% F1-score and 72.06% mIoU on the lane line semantic segmentation task, which runs at 82 FPS with merely 0.09M parameters and 13.0 GFLOPs. For road region detection, the mIoU and F1-score reach 97.62% and 98.86%, respectively. The results demonstrate that the proposed method enables accurate and robust road region detection in complex road environments with low computational costs. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111820 |
