Author(s)

Caixia Zhao ¹, Liang Shao ²

Affiliation(s)

¹ School of Air Transportation and Engineering, Nanhang Jincheng College, Nanjing, China
² AVIC General Research Institute Co.,Ltd, Zhuhai, China

Corresponding Author

Caixia Zhao

ABSTRACT

Lane detection is essential for autonomous driving and advanced driver-assistance systems, yet remains challenging in complex road scenes with occlusion, illumination variations, adverse weather, and ambiguous lane markings. To address these issues, we propose Context Feature Guided CLRNet (CFG-CLRNet), a vision-based lane detection framework built upon CLRNet. Specifically, Context Feature Guidance Module (CFGM) is introduced to enhance discriminative lane features by integrating coordinate attention with dual-branch atrous convolution, enabling effective multi-scale contextual interaction while preserving position-sensitive information. In addition, Geometric Consistency Loss (GCL) is designed to regularize lane geometry through second-order differences of lane points, improving structural continuity and geometric localization under incomplete or degraded visual observations. Extensive experiments on CULane and TuSimple demonstrate that CFG-CLRNet consistently improves lane detection performance over the baseline and exhibits stronger robustness in challenging road conditions, including occlusion, nighttime scenes, and curved lanes.

KEYWORDS

Lane Detection; Deep Learning; Context Feature; Geometric Consistency

CITE THIS PAPER

Caixia Zhao, Liang Shao. An Efficient Visual Lane Detection Method for Complex Road Scenes. Journal of Electronics and Information Science (2026). Vol. 11, No. 1, 84-94. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2026.110111.

REFERENCES

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[15] TuSimple: Tusimple benchmark. https://github.com/TuSimple/tusimple-benchmark/ (2020).

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