FLKFormer: Frequency-Enhanced Large-Kernel Framework for Object Detection in UAV Imagery

UAV object detection remains challenging due to large scale variation, dense small objects, frequent occlusion, and complex background interference. Existing CNN-based detectors are often limited by weak small-object representation, while Transformer-based detectors may not adequately preserve local details in dense aerial scenes. This paper proposes a dual-path detection framework that integrates frequency-domain enhancement with large-kernel convolution and Transformer-based global modeling. An FFT Large-Kernel Convolution (FFLKC) module is introduced to enhance high-frequency details and enlarge the effective receptive field. A Transformer pathway with Full-Process Feature Attention (FPFA) is designed to strengthen long-range dependency modeling and semantic representation. A Frequency-Semantic Memory-guided Adaptive Fusion (FMSAF) module is further employed to integrate local detail features and global contextual information. Experiments on UAVDT and VisDrone demonstrate that the proposed method achieves superior overall detection performance and stronger small-object perception than mainstream detectors. The method reaches 58.7 AP and 51.8 APS on UAVDT, and 39.4 AP and 30.5 APS on VisDrone. Qualitative and quantitative results verify the effectiveness of the proposed design in improving detection quality under complex UAV backgrounds.