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Dynamic Scale Perception-Driven Multi-UAV Collaborative 3D Object Detection Method
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DOI：10.11999/jeit251378.png)
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Abstract
En 中文
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 similar to 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.
Keywords:
Remote sensing images
Multi-UAV collaboration
3D object detection
Dynamic scale perception
Adaptive feature aggregation
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