Radatron: Accurate Detection Using Multi-Resolution Cascaded MIMO Radar
Sohrab Madani, Jayden Guan, Waleed Ahmed, Saurabh Gupta, Haitham Hassanieh
Abstract
"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 5 cm range resolution and 1.2-degree 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. Code and dataset are available on https://jguan.page/Radatron/."
BibTeX
@inproceedings{eccv2022_radatronaccurate,
title = {Radatron: Accurate Detection Using Multi-Resolution Cascaded MIMO Radar},
author = {Sohrab Madani and Jayden Guan and Waleed Ahmed and Saurabh Gupta and Haitham Hassanieh},
booktitle = {ECCV 2022},
year = {2022}
}