In the traditional air target detection algorithm,artificial design features are needed to meet the target detection requirements in different sky backgrounds,which poses a challenge to the automation and intelligence of future air defense combat system requirements.For this reason,in the context of the application of air defense weapon imaging system,the airborne infrared aircraft data set was established with reference to the PASCAL VOC2007 data set format.The Faster R-CNN algorithm and R-FCN algorithm based on convolutional neural network were applied to the issue of airborne infrared aircraft detection.Then,under the Caffe framework platform,the test set of self-built dataset is tested by using the Faster R-CNN + VGG16 / ResNet-101 model and the R-FCN+ResNet-101 model.The experimental results show that both Faster R-CNN and R-FCN algorithms can effectively detect infrared aircraft in the air when it is difficult to detect the small and weak targets,cloud cover,low contrast and target truncation.The good detection effect of this article provides a more concise way to solve the issue of airborne infrared aircraft detection.