DEADet: Detail Enhancement with Multi-level Feature Joint Perception for Aircraft Fine-grained Object Detection

Fine-grained aircraft detection in remote sensing images plays a crucial role in the field of military security, providing critical technical support for military reconnaissance, target tracking, and air defense early warning. However, the task suffers from both inadequate preservation of local features, which causes a loss of discriminative details, and a reliance on layer-specific feature interactions that fails to leverage cross-scale complementary information. To address these challenges, we propose a Detail Enhancement Aircraft Fine-grained Object Detection (DEADet), which is an efficient one-stage detector based on multi-level feature joint perception. First, to preserve fine-grained details, we design the Detail-Enhanced Feature Representation (DEFR) module based on a compress-enhance-reconstruct principle. Second, to achieve comprehensive cross-level feature perception and enhancement, we introduce the Multi-level Joint Feature Refiner (MJFR), which consists of a Multi-level Feature Fusion (MLFF) module and a Multi-level Feature Enhance (MLFE) module. Third, we propose the Normalized Angle and Center Loss (NAC Loss), incorporating both angle penalty and center penalty to improve orientation estimation and spatial localization, thereby guiding the model toward more precise feature learning. Extensive experiments on the FAIR1M-aircraft and MAR20 datasets demonstrate that our method achieves state-of-the-art performance with 88.87% and 85.51% mAP respectively, while maintaining high efficiency with only 23.07M parameters and 84.19G FLOPs, providing an effective solution for military remote sensing applications. The code is available at https://github.com/mysort/DEADet.