Fast Identification and Detection Algorithm for Maneuverable Unmanned Aircraft Based on Multimodal Data Fusion
Tian Luan, S. Kevin Zhou, Yicheng Zhang, Weijun Pan · Mathematics · 2025
To address the critical challenges of insufficient monitoring capabilities and vulnerable defense systems against drones in regional airports, this study proposes a multi-source data fusion framework for rapid UAV detection. Building upon the YOLO v11 architecture, we develop an enhanced model incorporating four key innovations: (1) A dual-path RGB-IR fusion architecture that exploits complementary multi-modal data; (2) C3k2-DATB dynamic attention modules for enhanced feature extraction and semantic perception; (3) A bilevel routing attention mechanism with agent queries (BRSA) for precise target localization; (4) A semantic-detail injection (SDI) module coupled with windmill-shaped convolutional detection heads (PCHead) and Wasserstein Distance loss to expand receptive fields and accelerate convergence. Experimental results demonstrate superior performance with 99.3% mAP@50 (17.4% improvement over baseline YOLOv11), while maintaining lightweight characteristics (2.54M parameters, 7.8 GFLOPS). For practical deployment, we further enhance tracking robustness through an improved BoT-SORT algorithm within an interactive multiple model framework, achieving 91.3% MOTA and 93.0% IDF1 under low-light conditions. This integrated solution provides cost-effective, high-precision drone surveillance for resource-constrained airports.