Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling
This work addresses robustness in lane detection for autonomous driving and ADAS, though it appears incremental as it builds on existing methods like CLRNet.
The paper tackles lane detection under adverse conditions like extreme weather and occlusions by proposing a Wavelet-Enhanced Feature Pyramid Network with adaptive preprocessing and attention-guided sampling, achieving significant performance improvements on benchmarks such as CULane and TuSimple.
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.