基于4D毫米波雷达的实时高精度3D目标检测新方法
RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging Radar
- 采用可重参数化网络提取多尺度特征，兼顾速度与内存效率
- 在View-of-Delft和TJ4DRadSet上分别达到50.48和32.30 mAP
- 推理速度超67帧/秒，适合车载实时系统部署
3D目标检测对自动驾驶与高级驾驶辅助系统至关重要。然而，现有方法多侧重精度，忽视实际应用中的推理速度。本文提出RadarNeXt，一种基于4D毫米波雷达点云的实时可靠3D目标检测器。通过可重参数化神经网络捕获多尺度特征，降低内存开销并加速推理。为突出雷达点云中不规则前景特征并抑制背景杂波，设计了多路径可变形前景增强网络（MDFEN），在保持高精度的同时避免参数量激增。在View-of-Delft与TJ4DRadSet数据集上的实验表明，使用MDFEN的RadarNeXt变体分别取得50.48和32.30 mAP；在RTX A4000 GPU上推理速度超过67.10 FPS，Jetson AGX Orin上达28.40 FPS。本研究为基于4D毫米波雷达的3D感知提供了新颖有效的范式。
原文摘要 · Abstract (English)
3D object detection is crucial for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). However, most 3D detectors prioritize detection accuracy, often overlooking network inference speed in practical applications. In this paper, we propose RadarNeXt, a real-time and reliable 3D object detector based on the 4D mmWave radar point clouds. It leverages the re-parameterizable neural networks to catch multi-scale features, reduce memory cost and accelerate the inference. Moreover, to highlight the irregular foreground features of radar point clouds and suppress background clutter, we propose a Multi-path Deformable Foreground Enhancement Network (MDFEN), ensuring detection accuracy while minimizing the sacrifice of speed and excessive number of parameters. Experimental results on View-of-Delft and TJ4DRadSet datasets validate the exceptional performance and efficiency of RadarNeXt, achieving 50.48 and 32.30 mAPs with the variant using our proposed MDFEN. Notably, our RadarNeXt variants achieve inference speeds of over 67.10 FPS on the RTX A4000 GPU and 28.40 FPS on the Jetson AGX Orin. This research demonstrates that RadarNeXt brings a novel and effective paradigm for 3D perception based on 4D mmWave radar.
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