Small-object detection in unmanned aerial vehicle （UAV） imagery remains one of the most challenging tasks in computer vision and is particularly important for surveillance，search and rescue，traffic monitoring，and precision agriculture. UAV images present several intrinsic difficulties： small targets often occupy fewer than 32 × 32 pixels in high-resolution images，exhibit low contrast against complex backgrounds，suffer from motion blur and varying illumination，and show extreme scale variation because of changing flight altitude. Traditional object detectors，which are primarily designed for ground-level imagery with relatively large objects，do not address these challenges well，because conventional convolutional neural networks progressively lose fine-grained spatial information through pooling and strided convolutions.

To overcome these limitations，we propose multi-scale frequency adaptive-you only look once（MSFA-YOLO），an innovative detector that integrates multi-scale feature extraction，frequency-domain analysis，and efficient feature enhancement. The architecture includes three key components. First，the multi-scale edge feature extractor module preserves and enhances edge information that is crucial for small-object detection. It processes input features through parallel pathways at multiple scales，applies the Sobel operator for edge extraction，and then fuses the resulting edge features with the original features through an adaptive cross-channel attention mechanism that dynamically adjusts their relative contributions. Second，the frequency-spatial adaptive module exploits the complementary nature of spatial and frequency-domain representations. It applies the fast Fourier transform to decompose spatial features into frequency components and uses a dual-branch design in which the spatial branch preserves location-specific information through depthwise separable convolutions，while the frequency branch applies learnable filters to selectively enhance relevant frequency bands. An adaptive gating mechanism then combines the two branches. Third，the lightweight feature fusion enhancement module addresses computational efficiency through a multi-branch structure with different kernel sizes and re-parameterizable convolutions，which are merged during inference without performance loss. Group convolution and channel shuffling are further introduced to reduce parameter count while maintaining representational capacity.

Comprehensive experiments were conducted on two challenging UAV datasets： VisDrone2019-DET，which contains 10 209 images with 54 200 annotated instances from ten object categories captured under diverse conditions，and UAVDT，which includes 80 000 frames with three vehicle categories and exhibits significant scale variation and occlusion. On VisDrone2019-DET，MSFA-YOLO achieved an mAP₅₀ of 38.1% with only 3.08 M parameters，representing a 4.3% improvement over the YOLO11n baseline （33.8%） and outperforming state-of-the-art lightweight methods including LWUAVDet （37.4% with 9.7 M parameters），Mamba-YOLO （36.4% with 5.66 M parameters），LUD-YOLOn （35.2% with 2.81 M parameters），and Hyper-YOLO （33.7% with 2.68 M parameters）. These results demonstrate a superior balance between efficiency and accuracy，with the highest detection accuracy among all compared methods while maintaining a compact model size. On UAVDT，MSFA-YOLO achieved an mAP₅₀ of 32.3% and an mAP_50-95 of 19.3%，surpassing the YOLO11n baseline （30.2% mAP₅₀ and 17.8% mAP_50-95） by 2.1% and 1.5%，respectively，while also outperforming Mamba-YOLO （31.7% mAP₅₀ with 5.66 M parameters），Hyper-YOLO （30.9% mAP₅₀），and YOLO12n （30.6% mAP₅₀）. Precision reached 45.1% and recall reached 31.2%，indicating robust detection performance across diverse scenarios. The proposed method achieves these results with only 3.08 M parameters，substantially fewer than Mamba-YOLO’s 5.66 M and comparable to the most efficient baseline models，validating the effectiveness of the overall design. Ablation studies confirmed the contribution of each component： the MEF module provided clear gains through edge enhancement，the FSA module improved representation through frequency-spatial fusion，and the LFFE module optimized the efficiency-accuracy trade-off while reducing computational overhead. Cross-dataset evaluation further demonstrated strong generalization，as models trained on VisDrone2019-DET achieved robust performance on UAVDT without fine-tuning，suggesting effective feature learning and domain adaptation.

This work presents MSFA-YOLO as a novel detection framework that addresses the core challenges of small-object detection in UAV imagery through the synergistic integration of multi-scale edge features，frequency-spatial adaptive processing，and efficient architectural design. The approach significantly improves performance over existing methods while maintaining the computational efficiency required for real-world UAV deployment. Its effectiveness stems from three main insights： preserving and enhancing edge information through multi-scale Sobel processing is crucial for targets with very limited pixel support； frequency-domain analysis provides complementary fine-grained cues that are often lost in purely spatial processing； and careful architecture design can maintain a compact model size without sacrificing detection accuracy. Future work will focus on improving robustness under extreme conditions such as low illumination and adverse weather，extending the framework to video-based detection with temporal consistency constraints，exploring self-supervised pre-training for domain adaptation，and deploying the system on edge devices with hardware-specific optimizations. Overall，the proposed method represents a meaningful step toward practical，deployable UAV-based detection systems for challenging real-world environments.