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<p>In the field of autonomous driving, a commonly employed method to enhance detection accuracy and robustness is the fusion of multi-sensor perception. The fusion of millimeter-wave radar and camera can effectively complement each other, providing sufficient semantic information while ensuring robustness against varying illumination and weather conditions, at a lower cost. In this paper, we focus on the fusion of millimeter-wave radar point cloud features and image features, proposing a multi-level multi-attention feature-level fusion method. By improving the DLA34 backbone network to expand the model’s receptive field, we fuse point cloud features at multiple levels with image features and utilize an improved feature pyramid to handle features of both modalities, ensuring good cross-channel information capture capability. Our model leverages the advantages of multi-level multi-attention, achieving an accuracy of 34.3% in the challenging nuScenes dataset, which is 1.9% higher than the second-best model, demonstrating promising performance.</p>

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A multi-level multi-attention mechanism millimeter-wave radar and camera fusion method for 3D object detection

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Zehua Miao,
  • Yinbei Li,
  • Zizhuo Wu,
  • Jiaqiang Yang,
  • Yuliang Ma

摘要

In the field of autonomous driving, a commonly employed method to enhance detection accuracy and robustness is the fusion of multi-sensor perception. The fusion of millimeter-wave radar and camera can effectively complement each other, providing sufficient semantic information while ensuring robustness against varying illumination and weather conditions, at a lower cost. In this paper, we focus on the fusion of millimeter-wave radar point cloud features and image features, proposing a multi-level multi-attention feature-level fusion method. By improving the DLA34 backbone network to expand the model’s receptive field, we fuse point cloud features at multiple levels with image features and utilize an improved feature pyramid to handle features of both modalities, ensuring good cross-channel information capture capability. Our model leverages the advantages of multi-level multi-attention, achieving an accuracy of 34.3% in the challenging nuScenes dataset, which is 1.9% higher than the second-best model, demonstrating promising performance.