RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model
Yahia Dalbah, Jean Lahoud, Hisham Cholakkal

TL;DR
RadarFormer is a lightweight, transformer-based radar object detection model that achieves high accuracy and speed, specifically designed for radar data in autonomous driving, outperforming existing methods in efficiency and effectiveness.
Contribution
The paper introduces RadarFormer, a novel radar-only object detection model utilizing transformers and a channel-chirp-time merging module to significantly reduce complexity while maintaining accuracy.
Findings
RadarFormer is twice as fast as state-of-the-art methods.
It uses only one-tenth of the parameters of comparable models.
The model achieves competitive accuracy on the CRUW radar dataset.
Abstract
The performance of perception systems developed for autonomous driving vehicles has seen significant improvements over the last few years. This improvement was associated with the increasing use of LiDAR sensors and point cloud data to facilitate the task of object detection and recognition in autonomous driving. However, LiDAR and camera systems show deteriorating performances when used in unfavorable conditions like dusty and rainy weather. Radars on the other hand operate on relatively longer wavelengths which allows for much more robust measurements in these conditions. Despite that, radar-centric data sets do not get a lot of attention in the development of deep learning techniques for radar perception. In this work, we consider the radar object detection problem, in which the radar frequency data is the only input into the detection framework. We further investigate the challenges…
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Taxonomy
TopicsAdvanced SAR Imaging Techniques · Radar Systems and Signal Processing · Geophysical Methods and Applications
