MFDAFF-Net: Multiscale Frequency-Aware and Dual Attention-Guided Feature Fusion Network for UAV Imagery Object Detection

Recently, the rapid advancement of the unmanned aerial vehicle (UAV) remote sensing technology has positioned object detection in UAV imagery as a prominent research domain. However, object detection models designed for conventional imagery often fail to achieve satisfactory detection accuracy due to the challenges of varying object scales and a high proportion of dense small objects in UAV imagery. Based on this observation, we devise a multiscale frequency-aware and dual attention-guided feature fusion network (MFDAFF-Net) for UAV imagery object detection. MFDAFF-Net integrates spatial domain multiscale feature fusion with frequency domain information augmentation, effectively improving the detection accuracy of objects with varying scales in UAV imagery. Specifically, we construct a multiscale frequency-aware feature pyramid network as the neck of the model, which facilitates thorough top-down fusion of multiscale features through a meticulously designed feature fusion architecture. Then, we design a dual attention-guided adaptive feature fusion network (DAAFFN) as the specific feature fusion strategy. The DAAFFN effectively enhances and fully integrates multiscale features by leveraging spatial-channel collaborative attention and interscale feature interactions. Moreover, a wavelet-inspired frequency-aware module (WFM) is proposed to disentangle high-frequency object details from low-frequency backgrounds, eventually improving the detection performance for dense small objects. Comprehensive experimental evaluations conducted on the VisDrone2019 and UAVDT datasets demonstrate that MFDAFF-Net substantially outperforms existing state-of-the-art UAV imagery object detection methods.