Object Detection with Swin Vision Transformers from Raw ADC Radar Signals

Abstract

Object detection utilizing frequency modulated continuous wave radar is becoming increasingly popular in the field of autonomous vehicles. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rain or snow. Thus, there is a necessity for fully autonomous systems to utilize radar sensing applications in downstream decision-making tasks, generally handled by deep learning algorithms. Commonly, three transformations have been used to form range-azimuth-Doppler cubes in which deep learning algorithms could perform object detection. This method has drawbacks, specifically the pre-processing costs associated with performing multiple Fourier Transforms and normalization. We develop a network utilizing raw radar analog-to-digital converter output capable of operating in near real-time given the removal of all pre-processing. We obtain inference time estimates one-fifth of the traditional range-Doppler pipeline, decreasing from $\SI{156}{\milli\second}$ to $\SI{30}{\milli\second}$, and similar decreases in comparison to the full range-azimuth-Doppler cube. Moreover, we introduce hierarchical Swin Vision transformers to the field of radar object detection and show their capability to operate on inputs varying in pre-processing, along with different radar configurations, \textit{i.e.}, relatively low and high numbers of transmitters and receivers. Our network increases both average recall, and mean intersection over union performance by $\sim 6-7\%$, obtaining state-of-the-art F1 scores as a result on high-definition radar. On low-definition radar, we note an increase in mean average precision of $\sim 2.5\%$ over state-of-the-art range-Doppler networks when raw analog-to-digital converter data is used, and a $\sim5\%$ increase over networks using the full range-azimuth-Doppler cube.