RTNH+: Enhanced 4D Radar Object Detection Network using Combined CFAR-based Two-level Preprocessing and Vertical Encoding

TL;DR

RTNH+ enhances 4D Radar object detection by introducing novel preprocessing and vertical encoding algorithms, significantly improving detection accuracy under various weather conditions.

Contribution

It proposes two novel algorithms, CCTP and VE, to preprocess and encode 4D Radar data, boosting detection performance over previous methods.

Findings

Achieves 10.14% improvement in AP3D at IoU=0.3

Achieves 16.12% improvement in AP3D at IoU=0.5

Demonstrates robustness under various weather conditions

Abstract

Four-dimensional (4D) Radar is a useful sensor for 3D object detection and the relative radial speed estimation of surrounding objects under various weather conditions. However, since Radar measurements are corrupted with invalid components such as noise, interference, and clutter, it is necessary to employ a preprocessing algorithm before the 3D object detection with neural networks. In this paper, we propose RTNH+ that is an enhanced version of RTNH, a 4D Radar object detection network, by two novel algorithms. The first algorithm is the combined constant false alarm rate (CFAR)-based two-level preprocessing (CCTP) algorithm that generates two filtered measurements of different characteristics using the same 4D Radar measurements, which can enrich the information of the input to the 4D Radar object detection network. The second is the vertical encoding (VE) algorithm that effectively encodes vertical features of the road objects from the CCTP outputs. We provide details of the RTNH+, and demonstrate that RTNH+ achieves significant performance improvement of 10.14\% in ${{AP}_{3D}^{IoU=0.3}}$ and 16.12\% in ${{AP}_{3D}^{IoU=0.5}}$ over RTNH.