DACG-Net: A Dual-Backbone and Context-Guided Fusion Network for Aerial UAV Detection

Edge computing frameworks support the large-scale deployment of ground-based drone detection systems in the lowaltitude economy by addressing the complexity and latency issues of traditional cloud architectures. However, the small size of drone targets and complex backgrounds reduce detection accuracy, while high-precision models are challenging to deploy on resourceconstrained edge devices. To tackle these issues, this paper presents DACG-Net, a detection network comprising a Dynamic Alignment Network (DANet) and a Context-Guided Feature Pyramid Network (CG-FPN). DANet consists of two distinct backbones with a shared Stem to eliminate redundant shallow feature computation. It introduces a Dynamic Alignment Fusion Module (DAFM) to address spatial misalignment in dual-backbone fusion, enhancing adaptability to diverse features. In CG-FPN, the fusion approach based on the Concat module in traditional feature pyramids lacks effective feature selection and inter-layer interaction. To improve this, a Context-Guided Fusion Module (CGFM) is designed for refined feature aggregation. Experiments on multiple datasets demonstrate the superior performance of DACG-Net, achieving mAP50 improvements of 5.8%, 6.7%, 6.6%, and 8% over baseline models. Moreover, it reduces parameters by 10.3% with only a 16.9% increase in computational complexity. DACG-Net runs at 18 FPS on embedded devices, satisfying real-time detection requirements.