A Drone Detection Network Based on Multi-Scale Feature Fusion and Content-Guided Attention Mechanism

To address the limitations of traditional algorithms in detecting small UAV targets, An improved YOLOv8-based UAV detection network is proposed. First, a re-parameterization. RepVGG-style backbone replaces the original YOLOv8 backbone to enable more detailed multi-scale feature extraction and fusion. Second, a content-guided attention fusion module (CGAFusion) is integrated into the neck network, dynamically adjusting the feature weights to enhance the focus on UAV-specific features. Finally, the normalized Wasserstein distance (NWD) is proposed to enhance regression loss optimization and minimize sensitivity to minor target position deviations. Experimental results show that the proposed network achieves a 2.7% improvement in mean average precision (mAP) over compared models.