Abstract: The detection of small-scale targets, characterized by limited available features and unclear textures, has perennially posed a challenge in the field of object detection. To address issues related to false positives and false negatives unmanned aerial vehicle（UAV） targets, we propose an improved UAV small-target detection algorithm, termed LASD-YOLOv5. This algorithm introduces a polarized selfattention mechanism to more accurately extract minute features, incorporates a weighted bidirectional feature pyramid network to replace the path aggregation network, thus enhancing the utilization of low-level features. Furthermore, it decouples the detection head to expedite model convergence. Additionally, to tackle the scarcity of small targets and incomplete scene coverage in existing UAV small-target datasets, we contribute a multi-scene, low-speed, small UAV target dataset（LASD-D）. The experimental results demonstrate that our proposed algorithm achieves an average precision of 98. 29% in LASD-D, surpassing the baseline network by 2. 87%. Notably, it outperforms mainstream algorithms such as YOLOv7、 YOLOv8 and QueryDet, effectively meeting the demands of UAV small-target detection applications.