Abstract
Detecting objects efficiently from radar sensors has recently become a popular trend due to their robustness against adverse lighting and weather conditions compared with cameras. This paper presents an efficient object detection model for Range-Doppler (RD) radar maps. Specifically, we first represent RD radar maps with multi-representation, i.e., heatmaps and grayscale images, to gather high-level object and fine-grained texture features. Then, we design an additional Adapter branch, an Exchanger Module with two modes, and a Primary-Auxiliary Fusion Module to effectively extract, exchange, and fuse features from the multi-representation inputs, respectively. Furthermore, we construct a supernet with various width and fusion operations in the Adapter branch for the proposed model and employ a One-Shot Neural Architecture Search method to further improve the model's efficiency while maintaining high performance. Experimental results demonstrate that our model obtains favorable accuracy and efficiency trade-off. Moreover, we achieve new state-of-the-art performance on RADDet and CARRADA datasets with mAP@50 of 71.9 and 57.1, respectively.
Abstract (translated)
最近，由于雷达传感器在恶劣光照和天气条件下的鲁棒性优于摄像头，从雷达传感器中高效检测物体已成为一种流行趋势。本文提出了一种针对范围-多普勒（RD）雷达地图的高效目标检测模型。具体而言，我们首先使用多种表示方式来表征RD雷达图：热力图和灰度图像，以收集高层次的对象特征及细粒度的纹理特征。接着，设计了一个额外的适配器分支、具有两种模式的交换器模块以及主辅助融合模块，分别有效地从多表示输入中提取、交换和融合特征。此外，我们构建了一个包含不同宽度和融合操作的超网络（supernet）用于所提出的模型，并采用了一次性神经架构搜索方法以进一步提高模型效率同时保持高性能。实验结果表明，我们的模型在准确性和效率之间取得了有利的平衡。另外，在RADDet和CARRADA数据集上，我们分别达到了mAP@50为71.9和57.1的新最先进性能水平。
URL
https://arxiv.org/abs/2509.01280
