mRadNet: A Compact Radar Object Detector with MetaFormer
This work addresses the need for lightweight models in real-time embedded automotive systems, representing an incremental improvement in radar object detection.
The paper tackles the problem of compact and efficient radar object detection for automotive systems by proposing mRadNet, which achieves state-of-the-art performance on the CRUW dataset with the least parameters and FLOPs.
Frequency-modulated continuous wave radars have gained increasing popularity in the automotive industry. Its robustness against adverse weather conditions makes it a suitable choice for radar object detection in advanced driver assistance systems. These real-time embedded systems have requirements for the compactness and efficiency of the model, which have been largely overlooked in previous work. In this work, we propose mRadNet, a novel radar object detection model with compactness in mind. mRadNet employs a U-net style architecture with MetaFormer blocks, in which separable convolution and attention token mixers are used to capture both local and global features effectively. More efficient token embedding and merging strategies are introduced to further facilitate the lightweight design. The performance of mRadNet is validated on the CRUW dataset, improving state-of-the-art performance with the least number of parameters and FLOPs.