动态尺度感知驱动的多无人机协同3D目标检测方法

段淑靖; 王智睿; 成培瑞; 付琨

doi:10.11999/JEIT251378

动态尺度感知驱动的多无人机协同3D目标检测方法

doi: 10.11999/JEIT251378 cstr: 32379.14.JEIT251378

段淑靖^{1, 2, 3},
王智睿^{1, 3, ,},
成培瑞^{1, 3},
付琨^{1, 3}

1.
中国科学院空天信息创新研究院北京 100190
2.
中国科学院大学电子电气与通信工程学院北京 100049
3.
中国科学院空天信息创新研究院目标认知与应用技术国家级重点实验室北京 100190

基金项目: 国家自然科学基金(62571515)

详细信息

作者简介:
段淑靖：女，博士生，研究方向为分布式遥感图像智能解译

王智睿：男，副研究员，研究方向为计算机视觉与遥感图像理解

成培瑞：男，副研究员，研究方向为遥感图像智能解译

付琨：男，研究员，研究方向为遥感大数据智能解译

通讯作者:
王智睿　zhirui1990@126.com

中图分类号: TN911.73; TP75
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- 文章访问数: 149
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- 被引次数: 0
出版历程
- 收稿日期: 2025-12-30
- 修回日期: 2026-02-24
- 录用日期: 2026-03-03
- 网络出版日期: 2026-03-14

Dynamic Scale Perception-Driven Multi-UAV Collaborative 3D Object Detection Method

DUAN Shujing^{1, 2, 3},
WANG Zhirui^{1, 3
, ,},
CHENG Peirui^{1, 3},
FU Kun^{1, 3}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy ofSciences, Beijing 100049, China
3.
Key Laboratory of Target Cognition and Application Technology(TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Nature Science Foundation of China (62571515)

摘要

摘要: 多无人机协同三维目标检测是低空智能感知领域的核心技术，鸟瞰图(BEV)特征表征范式为该任务提供了全局空间一致性支撑。但在实际应用中，受遥感图像目标尺度小、分布稀疏等特性影响，现有基于Transformer的BEV感知方法因采用全图同质化特征处理策略，会造成大量计算资源浪费，或者容易丢失小目标的精细特征，难以实现计算效率与检测精度的平衡。针对上述问题，该文提出一种适用于多无人机协同场景的动态尺度感知检测网络，核心思路是通过尺度差异化特征处理机制，实现计算效率与检测精度的协同优化。为此，设计两个核心创新模块：动态尺度感知BEV生成模块(DSBG)与自适应BEV特征协同聚合模块(ACFA)。其中，DSBG模块基于各无人机的特征图目标分布情况动态感知生成多分辨率BEV特征；ACFA模块对多分辨率BEV特征进行自适应加权融合，生成全局一致的协同BEV特征，再输入检测解码器完成目标预测。实验结果表明，所提网络在AeroCollab3D和Air-Co-Pred两个多无人机协同仿真数据集上均表现优异，平均预测精度(mAP)分别达到64.0%和80.6%，相较于其他先进方法分别提升1.5%和7.2%；同时计算成本最大降低41.6%，实现了计算效率与检测精度的高效平衡。
- 遥感图像 /
- 多无人机协同 /
- 目标检测 /
- 动态尺度感知 /
- 自适应特征融合
Abstract: Objective Multi-UAV collaborative 3D object detection is a core technology for low-altitude intelligent perception, and the Bird’s-Eye View (BEV) feature representation paradigm provides support for global spatial consistency. However, in practical UAV remote-sensing scenarios, targets are extremely small, sparsely distributed, and embedded in a large proportion of background regions. Existing Transformer-based BEV perception methods adopt a homogeneous full-image feature-processing strategy. This strategy not only wastes computing resources because of excessive computation in large background areas, but also tends to dilute small-target features with background noise, making it difficult to balance computational efficiency and detection accuracy. Meanwhile, multi-UAV collaboration requires cross-device information interaction to achieve view complementarity and information gain, but this process is prone to redundant information and even feature conflicts. Traditional fixed-weight aggregation methods cannot accurately identify effective information or suppress redundancy, resulting in poor consistency of global BEV features and reduced collaborative detection accuracy. Therefore, the development of a detection network that is adaptive to multi-UAV aerial scenarios is of clear practical value. Methods A dynamic scale-aware detection network is proposed for efficient and accurate 3D object detection through two core modules: the Dynamic Scale-aware BEV Generation (DSBG) module and the Adaptive Collaborative BEV-Feature Aggregation (ACFA) module. The network establishes an end-to-end pipeline of “multi-view image input-dynamic scale adaptive feature encoding-BEV space 3D detection” (Fig. 1). First, the observed images collected by each UAV are processed independently by a parameter-sharing ResNet-50 backbone network to generate feature maps with a consistent structure. The DSBG module then takes these feature maps as input, calculates the amplitude of feature responses in each spatial region through the Local Scale-Aware Unit, and estimates the target distribution. On this basis, differentiated BEV grid encoding is dynamically allocated: high-resolution dense grids are assigned to high-response target regions to preserve fine-grained features, whereas low-resolution sparse grids are assigned to low-response background regions to reduce invalid computation. At the same time, target query vectors with spatial position priors are generated. The ACFA module receives the multi-resolution BEV features generated by the DSBG module, concatenates the dual-resolution features from different UAVs in the channel dimension, upsamples the low-resolution features to align them with the high-resolution features, models the local correlations of two-scale features through 3×3 convolution, and obtains a globally consistent BEV feature map through element-wise weighted summation. Finally, the global BEV features are fed into the DETR decoder for 3D target prediction, with Focal Loss used for classification and Smooth L1 Loss used for regression (Eqs. 5$ \sim $6). Results and Discussions Extensive experiments are conducted on two public multi-UAV collaborative simulation datasets, AeroCollab3D and Air-Co-Pred. The results show that the proposed method achieves strong performance on both datasets. Compared with current state-of-the-art methods and baseline models, it not only improves mean Average Precision (mAP) by up to 7.2 percentage points, but also substantially reduces key evaluation metrics, including mean size error by more than 48%, mean localization error, and mean orientation error. In particular, clear advantages are observed in small-target detection and fine-grained category recognition, with pedestrian detection accuracy improved by nearly 10 percentage points. Ablation experiments verify the effectiveness of both the DSBG and ACFA modules. The proposed method steadily improves detection accuracy while significantly reducing computational cost by up to 41.6%, thereby achieving coordinated optimization of accuracy and efficiency. Visualization results (Fig. 3) show that the predicted bounding boxes have higher spatial alignment with the ground truth, effectively alleviating the common problems of target overlap and missed detection in traditional methods. Fig. 4 further illustrates the technical advantages of multi-UAV collaborative detection. Even for targets occluded by obstacles, the proposed method achieves efficient detection, thereby enhancing the comprehensive perception capability of the global region. Conclusions A dynamic scale-aware detection network is proposed for multi-UAV collaborative 3D object detection to address the core challenges of the efficiency-accuracy tradeoff and poor feature consistency in traditional methods. The DSBG module achieves dynamic matching between the BEV encoding scale and target distribution, thereby reducing redundant computation, whereas the ACFA module improves multi-scale and multi-view feature aggregation to ensure global feature consistency and accuracy. Experimental results on two datasets confirm that the proposed method outperforms existing advanced methods in detection accuracy, computational efficiency, and robustness. Future work will focus on optimizing dynamic scale-adjustment strategies with temporal information and exploring multi-sensor fusion with lightweight LiDAR data to improve detection stability in complex scenarios.
- Remote sensing images /
- Multi-UAV collaboration /
- 3D object detection /
- Dynamic scale perception /
- Adaptive feature aggregation

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图 1 动态尺度感知驱动的多无人机协同3D目标检测网络结构图

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图 2 自适应BEV特征协同聚合模块示意图

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图 3 AeroCollab3D数据集上鸟瞰图检测可视化结果

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图 4 AeroCollab3D数据集检测可视化结果

下载: 全尺寸图片幻灯片

表 1 AeroCollab3D数据集对比实验结果

方法	BEV网格大小	mAP↑(%)	mATE↓(m)	mASE↓	mAOE↓	Cost↓
BEVDet^[10]	128×128	55.4	0.512	0.196	0.498	4.712
BEVDet4D^[11]		58.7	0.499	0.102	0.317	4.712
BEVLongTerm^[10]		33.5	0.527	0.298	0.515	4.712
BEVDepth^[12]		59.9	0.489	0.106	0.495	4.712
Where2comm^[25]		52.3	0.473	0.199	0.415	4.712
UCDNet^[21]		62.5	0.487	0.188	0.399	4.712
本文方法	−	64.0	0.460	0.086	0.288	3.505

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表 2 AeroCollab3D数据集细粒度检测结果

目标类别	mAP↑(%)	mATE↓(m)	mASE↓	mAOE↓
小轿车	79.7	0.300	0.096	0.043
货车	64.2	0.515	0.089	0.049
公交车	57.6	0.493	0.070	0.050
行人	54.7	0.536	0.093	1.011

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表 3 Air-Co-Pred数据集对比实验结果

方法	BEV网格大小	mAP↑(%)	mATE↓(m)	mASE↓	mAOE↓	Cost↓
BEVDet^[10]	128×128	63.9	0.432	0.204	0.108	4.712
BEVDet4D^[11]		61.7	0.431	0.224	0.139	4.712
BEVLongTerm^[10]		69.7	0.330	0.186	0.129	4.712
Where2comm^[25]		72.1	0.295	0.186	0.075	4.712
UCDNet^[22]		73.4	0.323	0.182	0.051	4.712
本文方法	−	80.6	0.327	0.094	0.045	2.869

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表 4 AeroCollab3D数据集细粒度基线对比实验结果

方法	BEV网格大小	car_AP(%)	truck_AP(%)	bus_AP(%)	pedestrian_AP(%)	mAP↑(%)	交互传输比	Cost↓
基线模型	50×50	71.4	57.4	55.0	36.4	55.0	0.0625	2.000
基线模型	200×200	74.6	63.3	56.2	45.5	58.5	1.0000	6.000
+DSBG	−	72.7	62.3	54.8	35.3	56.2	0.1775	3.317
+DSBG+ACFA	−	79.7	64.2	57.6	54.7	64.0	0.1787	3.505

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