轻量级雷达目标检测模型，专为车载系统设计
mRadNet: A Compact Radar Object Detector with MetaFormer
- 采用元变换器块与U-net结构，融合局部与全局特征
- 参数最少、计算量最低，在CRUW数据集上性能领先
- 适合嵌入式车载系统，兼顾精度与实时性
调频连续波雷达在汽车工业中日益普及，其在恶劣天气下的鲁棒性使其成为高级驾驶辅助系统中雷达目标检测的理想选择。然而，这些实时嵌入式系统对模型的紧凑性和效率有严格要求，而此前工作对此关注不足。本文提出mRadNet，一种面向紧凑性的新型雷达目标检测模型。mRadNet采用带有元变换器块的U-net架构，通过可分离卷积和注意力标记混合器有效捕捉局部与全局特征。引入更高效的标记嵌入与合并策略，进一步优化轻量化设计。在CRUW数据集上的实验表明，mRadNet以最少的参数和最低的浮点运算量（FLOPs）达到最优性能，超越现有最先进方法。
原文摘要 · Abstract (English)
Frequency-modulated continuous wave radars have gained increasing popularity in the automotive industry. Their 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 fewest parameters and the lowest FLOPs.
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