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

Jingyu Wang,Yunyi Wu,Mingrui Ma,Peng-nian Huang,Zhenyu Ma,Hongmei Wang

Published 2026 in IEEE Geoscience and Remote Sensing Letters

ABSTRACT

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. First, the multiscale feature refinement module (MSRM) is presented to extract the intraclass differential feature of aircraft, in which bidirectional channel connections are applied to fuse the feature in both global and local manners. Second, the multibranch feature focusing module (MBFM) is established to obtain the fine-grained interclass features, which could expand the receptive field to enhance the discriminative characteristic between similar aircraft. Finally, a novel loss function, called aspect-ratio-approximated IoU (ARA-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.

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