In UAV traffic surveillance, small target detection faces challenges such as insufficient feature representation and low efficiency in multi-scale fusion. To address this, this study proposes YOLO-MAF, a lightweight detector. First, the multi-scale edge enhancement (MSEE) module strengthens edge information via an adaptive multi-scale receptive field. Second, the SEGE module combines soft nearest-neighbor interpolation (SNI) and enhanced group convolution (GSConvE) to improve cross-level alignment and fusion. Finally, the MASF-Head adopts dual attention to learn spatial-channel weights for adaptive multi-scale fusion. On VisDrone2019, YOLO-MAF achieves 45.6% mAP@0.5 and 29.4% mAP@0.5:0.95, improving the baseline by 7.3%and 6.4% with 50% fewer parameters, demonstrating effective small-object detection under UAV scenarios.