An improved YOLOv8n-based small target detection algorithm is proposed to address the problem of low detection accuracy caused by small，densely overlapping targets and diverse and complex scenes in drone aerial images.The improved deformable convolution is integrated with some C2f layers in the backbone network to enhance the network's adaptability to different target scale changes.The GC attention mechanism is introduced in the Neck part to enhance the network's feature extraction ability.In the Head part，a dynamic detection head，DyHead，is designed，which unifies the attention of three dimensions of scale，space，and task，to improve the detection effect of the detection head.The feature fusion structure and detection head of the original model are improved to effectively improve the network's detection accuracy for small targets and reduce the model parameter quantity.Experimental results on the VisDrone dataset show that:1) The mAP₅₀ of the improved model has increased by 6.0 percentage points compared with that of the base model，and the frame rate when deploying on hardware is 20 frames per second;and 2) The detection effect of this model is significantly improved，and the detection speed meets the real-time requirements for small target detection in UAV aerial images.