The existing object detection algorithms， influenced by complex environments and the complexity of network models， face challenges in effectively detecting distant unmanned aerial vehicles （UAVs）. This paper proposes an improved unmanned aerial vehicle （UAV） target detection algorithm based on YOLOv8. First， to address the challenge of detecting small unmanned aerial vehicle targets at long distances， a new ultra-small object detection layer is proposed， which integrates shallow features. In this approach， the largest target detection layer is removed to optimize target scale focus and reduce network complexity. Second， the Backbone part incorporates the GhostConv module to further decrease the model’s parameter count. Then， in the Neck part， the LSKA attention mechanism is integrated by replacing the Bottleneck section in the C2f module with LSKA， designing a new C2f-LSKA module to replace some C2f modules in the Neck， enhancing the model’s contextual awareness and spatial information processing ability. Lastly， WIoUv3 is used as the boundary loss function to further improve model accuracy. Experimental results show that， compared with the original model， the improved model increases precision （P） by 5.0%， recall （R） by 11.9%， and mAP@0.5 by 9.5% on a custom UAV dataset， while reduces the model’s parameter count and size by 68.9% and 65.1%， respectively.