MCD-Net: Robust Multi-UAV Cooperative Detection via Secondary Matching and Hybrid Fusion for Occluded Objects

Multi-Unmanned Aerial Vehicle (UAV) cooperative detection systems enhance perception by sharing object information from well-perceived UAVs to perception-limited UAVs via cross-view projection. However, such projections often suffer from misalignment due to environmental complexities and dynamic conditions, while existing fusion methods lack the robustness to handle these inaccuracies effectively. To address this issue, we propose a novel Multi-UAV Cooperative Detection Network (MCD-Net), which introduces a secondary matching method and a hybrid fusion strategy to mitigate the adverse effects of projection misalignment. The secondary matching method integrates both background and object features to refine the inter-view projection transformation matrix, improving the reliability of cross-view information supplementation. The hybrid fusion strategy combines (1) Confidence-Based Decision Fusion for initial screening; (2) a Region Consistency Measurement module to evaluate similarity before and after projection, eliminating inconsistent results; and (3) a Vehicle Parts Perception module to detect occluded objects in potential regions, reducing false detections. Additionally, we contribute a dedicated vehicle parts dataset to train the classifier within the perception module. Experimental results demonstrate that MCD-Net achieves significant improvements over single-UAV detection, with higher recall and F-score metrics. Specifically, the recall for occluded objects improves by an average of 9.88%, highlighting the robustness and effectiveness of our approach in challenging scenarios.