A multi-scale asymptotic feature fusion algorithm based on YOLOv8 is proposed to address the issues of missed and false detections due to the diversity of target scales, dense small targets, and complex background environments in remote sensing images. Firstly, the Res2C2f module combined with multi-scale residual network is constructed to capture the features of different scales more effectively. Secondly, the pyramid pooling module with cross-level connection is designed to improve the problem of insufficient feature extraction ability of the original pyramid pooling module. Then the multi-scale asymptotic feature fusion network is reconstructed to realize the exchange of multi-scale information, and the features of different levels are fully utilized to enhance the effect of feature fusion. Finally, a small target detection layer with a size of 160×160 is added to improve the detection effect of the model on small targets in dense scenes. In the DOTA dataset, compared with the baseline model, the accuracy, recall, and mean value of average accuracy of the proposed algorithm are improved by 4.8, 4.0 and 3.7 percentage points, respectively.