MFDAFF-Net: Multiscale Frequency-Aware and Dual Attention-Guided Feature Fusion Network for UAV Imagery Object Detection
Shu Tian, Bin Zhang, Lin Cao, Lihong Kang, Jing Tian, Xiangwei Xing, Bo Shen, Chunzhuo Fan, Kangning Du, Chong Fu, Ye Zhang
Abstract
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.