The use of UAV (Unmanned Aerial Vehicle) for image acquisition and target recognition has become more and more widely used in the military and civil fields. However, due to the high shooting height and the overhead view of UAV, the ground target in the image are volume smaller, shapes flatter, and they are always obscured by trees or houses. The current target recognition algorithms are not effective in the above scenarios. This thesis focuses on the fast and accurate recognition of ground vehicle targets collected by UAV. The main contents of the thesis are as follows. Firstly, by analyzing the development history and the characteristics of the target recognition task towards UAV, the main focus of this topic is summarized, which provides a basis for further research on the vehicle target recognition algorithm for the UAV. Secondly, most of the existing target recognition algorithms use artificially designed features, which are poor accuracy and slow in target recognition tasks for UAV. In view of the above situation, we have designed a new deep learning single-stage target recognition network named DRFP. This model uses the residual structure as the skeleton, and uses the feature pyramid structure to achieve feature fusion. At the same time, the cross-entropy function with adjustment factors is added to the loss function to achieve the focus on difficult samples, and uses Gaussian non-maximum suppression algorithm to improve the detection rate of the dense area. The comprehensive evaluation results on the VEDAI (Vehicle Detection in Aerial Imagery) data set show that the model has better recognition accuracy. Finally, in response to the requirements of real-time target recognition, we use model compression based on the DRFP model. But at the same time, model compression causes a decrease in recognition accuracy. After combining the attention mechanism and feature fusion, the recognition accuracy was greatly improved, and a real-time vehicle target recognition model for UAV aerial image was finally proposed. Evaluation results on multiple UAV aerial image data sets show that the method has better recognition accuracy and recognition speed.