一个改进的RODNet用于基于雷达和摄像机融合的物体检测
Manman Fan1, Xianpeng Wang1, Mingcheng Fu1
1School of Information and Communication Engineering, Hainan University, Haikou 570228, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一个统一的框架,以改善不同硬件的深度学习雷达检测. 该方法通过结合校准和自适应时间建模来提高性能,提高了跨设备的概括性.
科学领域:
- 雷达信号处理 雷达信号处理
- 深度学习用于传感器融合.
- 计算机视觉校准 计算机视觉校准
背景情况:
- 雷达检测的深度学习模型与硬件变化作斗争,限制了它们在现实世界中的应用.
- 雷达系统中的硬件异质性导致检测模型的概括性不佳.
研究的目的:
- 为基于深度学习的强大的雷达检测开发一个统一的框架.
- 提高雷达检测系统的跨设备通用化能力.
主要方法:
- 实施了一个统一的框架,将视角-n-点 (PnP) 校准与空间对齐的闭环验证相结合.
- 利用多范围 bin 校准和 chirp-wise Z-score 标准化来实现信号统计统一.
- 采用轻量级的全球时间适配器 (GTA),具有全球门和三点注意力,以确保特征一致性.
主要成果:
- 在ROD2021数据集上实现了86.32%的平均精度 (AP).
- 显著超过了E-RODNet基线的22.88个百分点.
- 在各种雷达平台上表现出强大的泛化,参数增加仅为0.96%.
结论:
- 拟议的框架有效地解决了雷达检测中的硬件异质性.
- 结合信号级标准化和功能级适应是改善跨设备通用化的关键.
- 该方法为在各种环境中部署深度学习雷达检测提供了一个有希望的解决方案.
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