Aiming at the difficulty of real-time detection and limited computing resources due to the scale change of drones in public safety areas such as playgrounds and parks， a network dynamic real-time detection method for drones， YOLO-Ads， is proposed to increase the robustness of network ability to detect drone change. Firstly， the drone data set was built independently. Secondly， a new MDDRDNet network was established with the lightweight network as the backbone to reduce the complexity of model calculation， and the coordinated attention mechanism module was introduced to strengthen the network’s attention to space and channels. Then， the mean clustering algorithm is used to regenerate the prior frame， and the optimization method combining multiple probes and multiple data sets is used in the selection of the prior frame， so that the regenerated prior frame matches the drone better. The idea of feature fusion and residual error establishes a new detector head to adapt to the detection of smaller-scale drones. Finally， a class activation mapping module is introduced into the detection module to generate a heat map， so as to observe the sensitivity of the network to changes in the scale of drones. At the same time， comparative experiments are conducted with the current mainstream networks SSD， CenterNet， YOLOv5， YOLOx， etc.， and different backbone networks ResNet， EfficientNet， VGGNet， etc. The experimental results show that the newly proposed algorithm has an average accuracy of 96.62% in the detection of scale-changing drones. Compared with the YOLOv4 algorithm， it is increased by 1.88% . The detection speed is 47 frames per second， which is 19 frames higher than that of the YOLOv4 algorithm. The memory occupied by the model is about 10.844 M， which is about one-sixth of the original memory. It reflects the timeliness and robustness of the method.