基于改进YOLOv5s的无人机航拍小目标检测算法研究OA

At present,UAV aerial photography target detection technology has been widely applied in military and civilian fields,but in complex scenes,small targets are dense and prone to false positives and missed detections.For this purpose,this paper proposes a small target detection algorithm for drone aerial photography based on improved YOLOv5s,which replaces two ordinary convolutions with grouped convolutions and decoupled detection heads instead of coupled detection heads.The P5 detection head in the original algorithm is removed,and a new P2 detection head is added to the PANet structure.The simulation results show that the improved algorithm has good detection performance,with mAP50 increased by 9.3%compared to the original algorithm,and can meet the real-time detection requirements of UAV.