Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling

• URL: http://arxiv.org/abs/2503.18631v1
• Date: Mon, 24 Mar 2025 12:49:47 GMT
• Title: Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling
• Authors: Kunyang Li, Ming Hou,
• Abstract summary: Lane detection is critical for autonomous driving and driver assistance systems.<n>We propose a Wavelet-Enhanced Feature Pyramid Net-work to address these challenges.<n> Experiments on CULane and TuSimple demonstrate that our approach signiffcantly outperforms baselines in challenging scenarios.
• Score: 2.453824332203939
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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