Military aircraft target detection in remote sensing images is of great significance for reconnaissance and early warning and intelligence analysis. In view of the challenges of complex image background, large target scale variation and dense distribution in this task, a lightweight detection model based on Faster R-CNN is proposed. The model uses residual split attention network to capture the global context information of target region features to improve the representation ability of the model; it uses deformable convolution to dynamically learn the deformation features of target region, adapt to targets of different scales and shapes; it uses the method of comparative experiment to streamline the backbone network, reduce the impact of too deep backbone network and too low sampling rate on small target detection, and improve the recognition speed of the model. In the target candidate box screening stage, Soft NMS algorithm is introduced to remove candidate boxes with high overlap according to the descending order of confidence, and reduce the miss detection rate of densely distributed targets. The experimental results show that the Faster R-CNN model proposed in this paper has a mAP0.5-0.95 of 77.1% when the number of parameters is 23.844 MB, and the detection speed reaches 43.7 frames per second. Compared with multiple mainstream models, it has better comprehensive performance.