As an application platform in target detection, unmanned aerial vehicles play an incomparable advantage and characteristics in reconnaissance missions. However, the limited memory and computing power of the UAV platform are difficult in detection model deployment and slow at detection speed. To solve the above problems, an improved model based on YOLOv4 is proposed. Firstly, in order to reduce the memory usage of the model and save computing resources, this paper improves the prediction layer of the original YOLOv4 model according to the characteristics of the target size. Secondly, the improved model is trained, and then sparse training and channel pruning on the scaling factor of the BN layer are made to reduce the memory usage of the model again to improve the detection speed. The experimental results show that with the detection results being basically the same, the memory usage of the improved model is reduced by 54%, and the FPS is increased by 35% compared with the original model, reaching 58 frames per second respectively.