Lane detection is critical for autonomous driving and advanced driver assistance systems (ADAS). While recent methods like CLRNet achieve strong performance, they struggle under adverse conditions such as extreme weather, illumination changes, occlusions, and complex curves. We propose a Wavelet-Enhanced Feature Pyramid Network (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 design an adaptive preprocessing module to enhance lane visibility under poor lighting. An attention-guided sampling strategy further refines spatial features, boosting accuracy on distant and curved lanes. Experiments on CULane and TuSimple demonstrate that our approach significantly outperforms baselines in challenging scenarios, achieving better robustness and accuracy in real-world driving conditions.