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Radar-camera fusion for 3D object detection with aggregation transformer

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Abstract

In recent years, with the continuous development of autonomous driving, monocular 3D object detection has garnered increasing attention as a crucial research topic. However, the precision of 3D object detection is impeded by the limitations of monocular camera sensors, which struggle to capture accurate depth information. To address this challenge, a novel Aggregation Transformer Network (ATNet) is introduced, featuring Cross-Attention based Positional Aggregation and Dual Expansion-Squeeze based Channel Aggregation. The proposed ATNet adaptively fuses radar and camera data at both positional and channel levels. Specifically, the Cross-Attention based Positional Aggregation leverages camera-radar information to compute a non-linear attention coefficient, which reinforces salient features and suppresses irrelevant ones. The Dual Expansion-Squeeze based Channel Aggregation utilizes refined processing techniques to integrate radar and camera data adaptively at the channel level. Furthermore, to enhance feature-level fusion, we propose a multi-scale radar-camera fusion strategy that integrates radar information across multiple stages of the camera subnet’s backbone, allowing for improved object detection across various scales. Extensive experiments conducted on the widely-used nuScenes dataset validate that our proposed Aggregation Transformer, when integrated into superb monocular 3D object detection models, delivers promising results compared to existing methods.

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Data availability and access

The data that support the findings of this study are openly available in nuScenes at https://www.nuscenes.org/nuscenes.

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Funding

This work was supported in part by the National Nature Science Foundation of China under Grant 62106158; in part by the Research and Development Program of Beijing Municipal Education Commission under Grant KM202210028007; and in part by the R&D Program of Beijing Municipal Education Commission (KZ20231002822).

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Authors and Affiliations

  1. Information Engineering College, Capital Normal University, Beijing, 100048, China

    Jun Li & Han Zhang

  2. Institute of Artificial Intelligence Education, Capital Normal University, Beijing, 100048, China

    Jun Li

  3. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China

    Zizhang Wu

  4. Computer Vision Perception Department, ZongMu Technology, Shanghai, 201203, China

    Tianhao Xu

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Conceptualization: Jun Li, Zizhang Wu; Methodology: Jun Li; Formal analysis and investigation: Zizhang Wu; Writing - original draft preparation: Han Zhang; Writing - review and editing: Tianhao Xu.

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Correspondence to Zizhang Wu.

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Li, J., Zhang, H., Wu, Z. et al. Radar-camera fusion for 3D object detection with aggregation transformer. Appl Intell 54, 10627–10639 (2024). https://doi.org/10.1007/s10489-024-05718-1

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