The current mainstream lightweight object detection models exhibit low detection accuracy in unmanned aerial vehicle (UAV) photography scenes. This study introduces a high-precision and lightweight aerial photography image object detection model based on YOLOv8s, named LEFE-YOLOv8. First, an enhanced feature extraction convolution (EFEConv) incorporating an attention mechanism was developed. It is integrated with partial channel convolution (PConv) and 1×1 convolution to create a lightweight enhanced feature extraction module. This integration augments the model's feature extraction capabilities and reduces the number of parameters and computational complexity. Subsequently, a lightweight dynamic upsampling operator module was incorporated into the feature fusion network, effectively addressing the information loss problem during the upsampling process in high-level feature networks. Finally, a detection head with multi-scale modules was designed to enhance the network model's multi-scale detection capabilities. The final experimental results demonstrate that, compared with the benchmark model, the improved model achieves an average accuracy of 42.3% and 83.9% on the VisDrone2019 and HIT-UAV datasets, respectively, with less than 10×10⁶ parameters. These results establish the model's suitability for aerial image object detection tasks.