Multi-Representation Adapter with Neural Architecture Search for Efficient Range-Doppler Radar Object Detection

• URL: http://arxiv.org/abs/2509.01280v1
• Date: Mon, 01 Sep 2025 09:06:53 GMT
• Title: Multi-Representation Adapter with Neural Architecture Search for Efficient Range-Doppler Radar Object Detection
• Authors: Zhiwei Lin, Weicheng Zheng, Yongtao Wang,
• Abstract summary: This paper presents an efficient object detection model for Range-Doppler radar maps.<n>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.<n>We employ a One-Shot Neural Architecture Search method to further improve the model's efficiency while maintaining high performance.
• Score: 16.02875655103583
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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