GFSR：用于可靠车道检测的几何保真度与空间细化
GFSR: Geometric Fidelity and Spatial Refinement for Reliable Lane Detection
AI总结：
针对现有车道检测方法中分类置信度与几何质量脱节、回归模块弱化采样点关联导致复杂场景性能下降的问题，提出包含LaneIoU引导的置信度校准和自适应门控位置细化的GFSR框架，在CULane和CurveLanes上取得最优结果。
摘要：
车道检测是自动驾驶和高级驾驶辅助系统中的一项关键感知任务。然而，现有方法在复杂真实场景中仍会退化，原因在于两个主要限制。首先，分类置信度仅表征车道先验的分类存在性，与几何质量无强相关性。如果仅基于该置信度进行阈值过滤和NMS，模型倾向于保留高置信度的车道先验，而消除那些置信度较低但几何表示更优的先验。其次，现有方法中的回归模块削弱了采样点之间的相关性，阻碍了对远处、高曲率和复杂拓扑车道的细粒度优化，导致欠拟合。为解决这些问题，我们提出了几何保真度与空间细化（GFSR），一个由LaneIoU引导的置信度校准（LCC）和自适应门控位置细化（AGLR）组成的框架。具体地，LCC采用LaneIoU作为软监督来显式估计车道先验的几何保真度，并将其与分类置信度融合以构建协同可靠性指数（CRI）。该指数引导车道先验过滤，有效保留那些具有高分类置信度和良好几何质量的先验。同时，在每个细化阶段与回归头协作，AGLR预测采样点横向偏移并采用门控机制自适应调节校正幅度，增强点间相关性，提升模型对复杂车道场景的适应性和鲁棒性。在CULane和CurveLanes上的大量实验表明，我们的GFSR在CULane上达到了最优性能，F1_50和F1_75分数分别为81.46%和65.01%，在CurveLanes上达到了87.35%的F1_50。
英文摘要：
Lane detection stands as a crucial perception task in autonomous driving and advanced driver assistance systems. However, existing methods still degrade in complex real scenarios due to two major limitations. First, classification confidence only characterizes the categorical existence of lane priors and has no strong correlation with geometric quality. If threshold filtering and NMS are conducted merely based on this confidence, the model tends to retain lane priors with high confidence while eliminating those with lower confidence but superior geometric representation. Secondly, the regression modules in existing methods weaken correlations among sampling points, hindering fine-grained optimization of distant, high-curvature and complex-topology lanes and causing underfitting. To address these issues, we propose Geometric Fidelity and Spatial Refinement (GFSR), a framework consisting of LaneIoU-guided Confidence Calibration (LCC) and Adaptive Gated Location Refinement (AGLR). Specifically, LCC adopts LaneIoU as soft supervision to explicitly estimate the geometric fidelity of lane priors, which is further fused with classification confidence to construct the Collaborative Reliability Index (CRI). This index guides lane prior filtering, effectively retaining those with high classification confidence and favorable geometric quality. Meanwhile, cooperating with regression heads in each refinement stage, AGLR predicts sampling point lateral offsets and adopts a gating mechanism to adaptively regulate correction magnitude, strengthen inter-point correlations and boost model adaptability as well as robustness toward complex lane scenarios. Extensive experiments on CULane and CurveLanes demonstrate that our GFSR achieves state-of-the-art performance on CULane, with F1_50 and F1_75 scores of 81.46% and 65.01%, and reaches 87.35% F1_50 on CurveLanes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
