Aiming at the problems of low feature extraction capability and scale diversity in UAV aerial images, an improved YOLOv8 object detection algorithm for UAV aerial images is proposed. Firstly, P2 layer is added to enhance the small target detection capability of the model. Secondly, the bidirectional feature alignment fusion method is designed to improve the neck. Combining the idea of feature alignment module and bidirectional feature pyramid, the multi-scale fusion capability of the model is improved to achieve a more complete feature fusion. Then, bi-level routing-spatial attention module is designed and added to the backbone. By connecting the bi-level routing attention module and spatial attention module, the feature capturing ability of the target is strengthened. Finally, the loss function Focaler-XIoU is designed to solve the influence of sample difficulty distribution on border regression, and enhance the stability and detection effect of the model. The experimental results show that the improved network model has improved the VisDrone dataset mAP50 by 9.2%, which has better detection effect than the current mainstream target detection algorithm, and can well complete the UAV aerial image detection task.