Millimeter wave (mmWave) radars are becoming a more popular sensing modality in self-driving cars due to their favorable characteristics in adverse weather. Yet, they currently lack sufficient spatial resolution for semantic scene understanding. In this paper, we present Radatron, a system capable of accurate object detection using mmWave radar as a stand-alone sensor. To enable Radatron, we introduce a first-of-its-kind, high-resolution automotive radar dataset collected with a cascaded MIMO (Multiple Input Multiple Output) radar. Our radar achieves a 5 cm range resolution and 1.2◦ angular resolution, 10× finer than other publicly available datasets. We also develop a novel hybrid radar processing and deep learning approach to achieve high vehicle detection accuracy. We train and extensively evaluate Radatron to show it achieves 92.6% AP50 and 56.3% AP75 accuracy in 2D bounding box detection, an 8% and 15.9% improvement over prior art respectively.
Video:
Paper:
Accurate Detection Using Multi-Resolution Cascaded MIMO Radar
Sohrab Madani*, Junfeng Guan*, Waleed Ahmed*, Saurabh Gupta, Haitham Hassanieh
European Conference on Computer Vision (ECCV), 2022
* indicates equal contribution
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@InProceedings{Madani2022Radatron,
author=”Madani, Sohrab
and Guan, Junfeng
and Ahmed, Waleed
and Gupta, Saurabh
and Hassanieh, Haitham”,
editor=”Avidan, Shai
and Brostow, Gabriel
and Ciss{\’e}, Moustapha
and Farinella, Giovanni Maria
and Hassner, Tal”,
title=”Radatron: Accurate Detection Using Multi-resolution Cascaded MIMO Radar”,
booktitle=”Computer Vision — ECCV 2022″,
year=”2022″,
publisher=”Springer Nature Switzerland”,
address=”Cham”,
pages=”160–178″,
}