Aug 2026· Computers· Vol 15, pp. 561· 0 citations· 7 references
TL;DR
This work proposes YOLOv8n-LaneDG based on YOLOv8n-seg, a high-precision, deployable lane detection solution for vehicle-end platforms that outperforms mainstream lightweight models and matches heavy YOLOv8s-seg with far fewer parameters.
Abstract
Lane detection is a core perception task for Advanced Driver Assistance Systems (ADAS) and autonomous driving. Current methods struggle to balance accuracy, model complexity and inference efficiency: high-precision models rely on heavy modules with excessive computation, while lightweight ones suffer from weak feature extraction and low precision. To alleviate this inherent trade-off, we propose YOLOv8n-LaneDG based on YOLOv8n-seg. We design a dual-path gated fusion block to strengthen lane features and an efficient upsampling convolution block to reduce computational overhead, and we further design a weighted continuity loss to preserve lane structural integrity. Evaluated on TuSimple, our method lifts mAP@0.5 from 74.3% to 95.2%. It outperforms mainstream lightweight models and matches heavy YOLOv8s-seg with far fewer parameters, delivering a high-precision, deployable lane detection solution for vehicle-end platforms.
Lane detection is a critical task for autonomous driving and advanced driver assistance systems. Existing methods often suffer from insufficient fusion of global structure and local details, as well as underutilization of geometric priors such as vanishing point convergence. This work presents a structure-aware feature...
Zhi-Qiang Zeng, Long-Pei Wu, Xiao-Dong Wang et al.· Discover Computing· 0 citations
Efficient lane detection is essential for autonomous driving because lane markings are thin, sparse, and vulnerable to shadow, occlusion, road wear, and background road markings. This paper presents an ENet-based lane segmentation method enhanced by a compact attention-refinement block. The block is explicitly defined...
Zhi-Peng Tong· International Conference on...· 0 citations
The results presented in this study show promising potential to integrate into a driver-assistance system, although the data used here is limited to a proof-of-concept validation on the KITTI dataset.
Amit Pimpalkar, Pranali Dandekar, Harika Vanam et al.· Scientific Reports· 0 citations
A novel Vertical Noise Mitigation Module embedded in a hybrid end-to-end CNN architecture that minimises noise interference in the image, thereby increasing the robustness of lane marking detection and its effectiveness under adverse lighting conditions is highlighted.
Nima Zarbakht, Ju-Jia Zou, Gu Fang· Multimedia tools and applica...· 0 citations
The complexity of real-world scenes poses significant challenges to lane detection. Existing methods often sacrifice inference speed and model simplicity by relying on excessive anchors or complex associations. To tackle these issues, we propose SDLane, a highly efficient, accurate, and concise lane detection model....
Zhi-Yong Yang, Ziqiang Zhang, Yu Zhou et al.· Transportation Research Reco...· 0 citations
With the continuous development of autonomous driving technology, vehicle collision warning systems are playing an increasingly important role in this field, promoting the progress and improvement of the whole autonomous driving field. But existing methods have low detection rates and unstable multi-target tracking per...
Xiao-Yu Qin, Wei Li· SAE technical paper series· 0 citations
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