RFA-Net: A Feature Refining-Focused Network for Aircraft Fine-Grained Recognition

Fine-grained aircraft recognition is a crucial task in remote sensing scenarios, while the challenge is to distinguish the high visual similarity among different categories. However, the large variations within the same category of aircraft could cause a severe recognition accuracy decrease. To solve the problem, an aircraft fine-grained recognition approach based on the Refining-Focused Network (RFA-Net) is proposed. Firstly, the Multi-scale Feature Refinement Module is presented to extract the intra-class differential feature of aircraft, in which bidirectional channel connections is applied to fuse the feature in both global and local manner. Secondly, the Multi-branch Feature Focusing Module is established to obtain the fine-grained inter-class features, which could expand the receptive field to enhance the discriminative characteristic between similar aircraft. Finally, a novel loss function which called Aspect-Ratio-Approximated IoU is specifically designed to improve the aircraft fine-grained recognition accuracy, while the convergence of predicted bounding boxes could be expedited as well. Experimental results show that the proposed approach outperforms other competitors.