The detection of drone aerial images has become a research hotspot due to the extensive application of drones in various fields. However, the presence of a large number of small objects and complex scenes in the images poses severe challenges to the detection task. As an emerging technology, dynamic neural networks, with their input adaptive adjustment mechanism, provide important theoretical and technical support for solving the problem of detecting small objects in complex scenes of drone aerial images. In this work, we propose Dynamic You Only Look Once Object Detector (Dynamic-YOLO). Firstly, we improve You only look once (YOLO) v9. By leveraging the Convolutional Block Attention Module (CBAM) attention mechanism and Space-to-Depth Convolution (SPD-Conv), we optimise its backbone network to enhance the model's feature extraction ability. Then, we adjust the structure of its neck network, delete the detection head P5 for large objects, and add the detection head P2 for tiny objects. After that, we combine a dynamic router with the improved YOLOv9 to form a dynamic detector, achieving adaptive processing of images. The experimental results on the Vision Meets Drone (VisDrone) benchmark dataset have demonstrated the excellent performance and flexibility of Dynamic-YOLO. When the Floating Point Operations (FLOPs) increase to 101.5 G, the highest mean Average Precision at Intersection over Union 0.5:0.95 (mAP@0.5:0.95) reaches 32.3% and mAP@0.5 hits 51%, representing a 4.0% and 10% improvement over YOLOv9-M. In addition, by dynamically adjusting the parameters of the router, the computational resource consumption and detection accuracy of the model can be flexibly adjusted, effectively achieving an optimised balance between detection performance and resource costs.