A Fine-grained Recognition Model of Air Targets Based on Bilayer Faster R-CNN with Feedback

摘要

The accuracy of air target identification is of great significance for air defense operations and civilian management. A finegrained recognition model of aerial target based on bilayer faster regions with convolution neural network (Faster R-CNN) with feedback is proposed in the paper. Faster R-CNN model is a typical target detection model based on deep learning. However, its ability to distinguish categories with subtle differences is not enough. In the proposed model. Faster R-CNN model is used for the first training to get a classification model and the clustering analysis of the classification result is used to get confused categories. Then the first training model is fine-tuned to retrain the confusing categories. The model is tested in the FGVC-Aircraft-2013b data set, and the average training accuracy is raised from 88.7% to 89.3%, the accuracy of the classification is raised from 88.98% to 91.21%, which shows that this model is effective in improving the fine-grained identification of air targets.