To address the safety hazards posed by unauthorized "black flight" of unmanned aerial vehicle (UAV) clusters, as well as the limitations of existing detection methods in tiny target recognition and complex environment adaptability, this paper proposes a real-time detection and counting method based on a feature fusion framework. The core contribution of this work is a novel multi-level fusion architecture that aggregates multi-scale visual features to enhance detection robustness under challenging conditions. First, we construct a multi scene UAV data set containing 7,113 images, and design a dedicated feature fusion module based on improved YOLOv9 backbone. The Haar Wavelet Down-sampling (H... More >