提升复杂路况下车道线检测精度，尤其在恶劣光照与遮挡时表现更优。
Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling
- 用小波非局部块增强特征金字塔的全局上下文建模能力。
- 在CULane和TuSimple数据集上，远距离及弯曲车道检测精度显著提升。
- 适合自动驾驶与智能驾驶系统在真实复杂场景中的应用。
车道线检测对自动驾驶和高级驾驶辅助系统至关重要。尽管近期方法如CLRNet表现优异，但在极端天气、光照变化、遮挡及复杂曲线等条件下仍存在困难。本文提出一种基于小波的特征金字塔网络（WE-FPN），在特征金字塔前引入小波非局部模块，增强对遮挡和弯曲车道的全局上下文建模能力。同时设计自适应预处理模块，改善弱光环境下的车道可见性；并采用注意力引导采样策略，细化空间特征，提升远距离及弯曲车道的检测精度。在CULane和TuSimple数据集上的实验表明，该方法在挑战性场景下显著优于基线模型，具备更强的鲁棒性和实际应用价值。
原文摘要 · Abstract (English)
Lane detection is critical for autonomous driving and ad-vanced driver assistance systems (ADAS). While recent methods like CLRNet achieve strong performance, they struggle under adverse con-ditions such as extreme weather, illumination changes, occlusions, and complex curves. We propose a Wavelet-Enhanced Feature Pyramid Net-work (WE-FPN) to address these challenges. A wavelet-based non-local block is integrated before the feature pyramid to improve global context modeling, especially for occluded and curved lanes. Additionally, we de-sign an adaptive preprocessing module to enhance lane visibility under poor lighting. An attention-guided sampling strategy further reffnes spa-tial features, boosting accuracy on distant and curved lanes. Experiments on CULane and TuSimple demonstrate that our approach signiffcantly outperforms baselines in challenging scenarios, achieving better robust-ness and accuracy in real-world driving conditions.
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