Liu, Jianan, Zhao, Qiuchi, Xiong, Weiyi, Huang, Tao, Han, Qing-Long, and Zhu, Bing (2024) SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar. IEEE Transactions on Intelligent Vehicles, 9 (1). pp. 799-812.

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Abstract

The 4D millimeter-Wave (mmWave) radar is a promising technology for vehicle sensing due to its costeffectiveness and operability in adverse weather conditions. However, the adoption of this technology has been hindered by sparsity and noise issues in radar point cloud data. This paper introduces spatial multi-representation fusion (SMURF), a novel approach to 3D object detection using a single 4D imaging radar. SMURF leverages multiple representations of radar detection points, including pillarization and density features of a multidimensional Gaussian mixture distribution through kernel density estimation (KDE). KDE effectively mitigates measurement inaccuracy caused by limited angular resolution and multipath propagation of radar signals. Additionally, KDE helps alleviate point cloud sparsity by capturing density features. Experimental evaluations on View-of-Delft (VoD) and TJ4DRadSet datasets demonstrate the effectiveness and generalization ability of SMURF, outperforming recently proposed 4D imaging radarbased single-representation models. Moreover, while using 4D imaging radar only, SMURF still achieves comparable performance to the state-of-the-art 4D imaging radar and camera fusion-based method, with an increase of 1.22% in the mean average precision on bird’s-eye view of TJ4DRadSet dataset and 1.32% in the 3D mean average precision on the entire annotated area of VoD dataset. Our proposed method demonstrates impressive inference time and addresses the challenges of real-time detection, with the inference time no more than 0.05 seconds for most scans on both datasets. This research highlights the benefits of 4D mmWave radar and is a strong benchmark for subsequent works regarding 3D object detection with 4D imaging radar.

Item ID:	80688
Item Type:	Article (Research - C1)
ISSN:	2379-8904
Copyright Information:	© 2023 IEEE.
Date Deposited:	11 Oct 2023 22:56
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 40% 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 40% 40 ENGINEERING > 4002 Automotive engineering > 400204 Automotive safety engineering @ 20%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 90% 27 TRANSPORT > 2703 Ground transport > 270311 Road safety @ 10%
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