提出自适应光照融合网络，提升夜间道路分割精度。
IAF-Net: Illumination-Adaptive Fusion for Low-Light Urban Road Segmentation
- 根据光照动态调整RGB与几何特征融合权重
- 在夜间数据集上达到最优的MaxF指标（+0.70%）
- 适合自动驾驶夜间/恶劣天气场景应用
语义道路分割对自动驾驶至关重要，但在低光条件下现有方法性能显著下降。许多多模态融合方法未显式适应模态可靠性随光照变化的特性，导致夜间劣化后的RGB特征被引入融合表示。本文提出IAF-Net（光照自适应融合网络），一种端到端框架，通过核心光照自适应融合（IAF）模块动态调节RGB与几何特征的融合权重，并利用亮度调制注意力解码器增强低光特征选择。同时构建两个专用数据集：nuScenes夜间道路分割（nuScenes-NRS）和CARLA多天气道路分割（CARLA-MWRS）。在nuScenes-NRS上的实验表明，该方法在对比方法中表现最优；CARLA-MWRS进一步验证了其在恶劣天气下的鲁棒性。在40%训练子集上的消融研究显示，IAF模块单独贡献最大，使MaxF提升0.70%。
原文摘要 · Abstract (English)
Semantic road segmentation is important for autonomous driving, but existing methods suffer severe performance degradation under low-light conditions. Many existing multi-modal fusion methods do not explicitly adapt to illumination-dependent changes in modality reliability, which can propagate degraded RGB features into the fused representation at night. We propose IAF-Net (Illumination-Adaptive Fusion Network), an end-to-end framework with illumination-adaptive fusion for robust road segmentation across different lighting conditions. It dynamically adjusts fusion weights of RGB and geometric features via the core Illumination-Adaptive Fusion (IAF) module, and enhances low-light feature selection with a brightness-modulated attention decoder. We also construct two dedicated datasets: nuScenes Nighttime Road Segmentation (nuScenes-NRS) and CARLA Multi-Weather Road Segmentation (CARLA-MWRS). Experiments on nuScenes-NRS show state-of-the-art overall performance among the compared methods, while CARLA-MWRS further validates robustness across adverse weather conditions. Ablation studies on a 40% training subset further highlight the importance of the IAF module, which provides the largest individual gain of 0.70% in MaxF.
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