Aiming at the problems of remote sensing images, such as complex background environment, low resolution and insufficient feature information, which lead to low target detection precision, this study proposes a small target detection algorithm based on improved YOLOv8n, REI-YOLOv8n. First, in the backbone extraction network, a refined feature extraction module is designed to strengthen the network's ability of capturing subtle features to differentiate between complex backgrounds. Second, an enhanced feature fusion network framework, EiFPN, is constructed in the neck network, and an attention-based fusion module is designed to realize the efficient fusion of multi-scale features to improve the performance of small target detection. In addition, in order to fully utilize the high-resolution spatial information, a fourth detection layer is added to further strengthen the detection capability of small targets. Finally, a new bounding box regression loss Inner-PIoU is designed to enhance the localization performance of the model and accelerate the convergence speed. The experimental results show that the mAP@0.5 (mean average precision at 50% intersection over union) of the improved REI-YOLOv8n algorithm on the datasets NWPU VHR-10 and Visdrone2019 reaches 0.904 and 0.369, respectively, which is 3.7% and 6.0% higher than that of the YOLOv8n, and effectively improves the detection performance of the network on small targets in remote sensing.