用运动流提升雷达检测，解决噪声和伪影难题
RaFD: Flow-Guided Radar Detection for Robust Autonomous Driving
- 通过帧间鸟瞰图运动流辅助检测，融合几何信息
- 在RADIATE数据集上达到当前最优性能
- 适合做雷达感知的算法研究者参考
雷达在自动驾驶中展现出强大潜力，但原始雷达图像常受噪声和“鬼影”伪影干扰，仅依赖语义特征进行目标检测极为困难。为此，我们提出RaFD，一种基于雷达的目标检测框架，通过估计帧间鸟瞰图（BEV）运动流，并利用其几何线索增强检测精度。具体而言，设计了一个与检测网络联合训练的监督式运动流估计辅助任务，利用估计的运动流将前一帧特征传播至当前帧。该基于运动流引导的纯雷达检测器在RADIATE数据集上取得当前最优表现，凸显了引入几何信息对解析本质模糊的雷达信号的重要性。
原文摘要 · Abstract (English)
Radar has shown strong potential for robust perception in autonomous driving; however, raw radar images are frequently degraded by noise and "ghost" artifacts, making object detection based solely on semantic features highly challenging. To address this limitation, we introduce RaFD, a radar-based object detection framework that estimates inter-frame bird's-eye-view (BEV) flow and leverages the resulting geometric cues to enhance detection accuracy. Specifically, we design a supervised flow estimation auxiliary task that is jointly trained with the detection network. The estimated flow is further utilized to guide feature propagation from the previous frame to the current one. Our flow-guided, radar-only detector achieves achieves state-of-the-art performance on the RADIATE dataset, underscoring the importance of incorporating geometric information to effectively interpret radar signals, which are inherently ambiguous in semantics.
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