RadarNeXt: Real-Time and Reliable 3D Object Detector Based On 4D mmWave Imaging Radar
This addresses the need for real-time, reliable 3D perception in autonomous driving systems, though it appears incremental as it builds on existing radar-based detection methods.
The paper tackles the problem of balancing accuracy and speed in 3D object detection for autonomous driving by proposing RadarNeXt, a detector based on 4D mmWave radar point clouds, achieving 50.48 and 32.30 mAPs on datasets with inference speeds over 67.10 FPS on high-end hardware.
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.