Detecting small objects in UAV aerial imagery remains a challenging task, primarily due to significant information loss during feature downsampling in existing models, insufficient multi-scale feature fusion, and poor alignment between shallow and deep features. To address these issues, we propose an improved model based on YOLOv8s, named LBTA-YOLOv8. Specifically, we introduce a novel LBTM downsampling mechanism that incorporates both appearance similarity and spatial proximity to guide the feature compression process while effectively preserving key information of small objects. Additionally, we employ TAFNet to enhance the interaction between shallow detail features and deep semantic features. Within TAFNet, we design a 3D-GLFF module based on coarse-grained block attention to achieve better alignment and fusion of multi-scale features, solving the problem of feature misalignment and inconsistency in existing fusion methods. We also propose a three-level fusion strategy to comprehensively integrate information from different scales. Extensive experiments on two UAV object detection benchmarks demonstrate that our method outperforms existing mainstream approaches and achieves state-of-the-art performance. For instance, LBTA-YOLOv8 achieves 0.434 mAP50 and 0.257 mAP50-95 on the VisDrone2021 dataset.