## 5 Conclusions  
As satellite imaging technology and deep learning technology have developed, remote sensing object detection has become a popular research topic. To address the problems of complex scenes, large scenes with small objects, and large-scale differences of objects in remote sensing object detection, a dense feature pyramid network based on YOLO known as DFPN-YOLO was proposed in this paper.  
First, we added an attention module to the residual blocks of the backbone to allow the network to quickly extract key feature information in complex scenes. Then, we added a larger detection layer to address the difficulty of detecting small objects in large fields of view. Finally, we proposed a dense feature pyramid network structure named Dense-FPN, which enabled all four detection layers to combine the semantic information, improving the object detection performance at different scales. Our proposed method achieves a high accuracy on the RSOD datasets and DIOR datasets and outperforms both classical algorithms and even outperforms the YOLOv4 in terms of the mAP metric. On the DIOR datasets, our algorithm achieves a maximum mAP of 69.33%, which is considerably higher than the 62.41% mAP of YOLOv3, and due to the Dense-FPN structure, the detection accuracy of our algorithm is higher than the accuracies of other algorithms in most object categories. On the RSOD datasets, the precision of our algorithm is better than the performance of other classical algorithms, reaching an mAP of 92%, which is 8% higher than the mAP of 83.9% of YOLOv3. From the comparison experiments, we found that YOLOv4 with an FPN + PAN structure and DFPN-YOLO with a Dense-FPN structure significantly outperformed YOLOv3 in terms of overall performance, demonstrating the importance of feature fusion for detection precision. Furthermore, our method performed slightly better than YOLOv4.  
However, although our method achieves good performance on the RSOD datasets and DIOR datasets, it has a poor detection performance on some high-noise remote sensing images, and the detection of blurred images and high-noise remote sensing images remains a major challenge for remote sensing object detection. We will carry out additional research in future work.  
Conceptualization, Y.S. and F.B.; methodology, Y.S. and F.B.; software, Y.S. and Y.G.; validation, F.B., W.L., and X.H.; formal analysis, X.H.; investigation, X.H.; data curation, Y.G.; writing--original draft preparation, Y.S.; writing--review and editing, F.B. and W.L.; supervision, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61971006).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Exp 1** & **Exp 2** & **Exp 3** & **Exp 4** \\ \hline SGE & & \(\surd\) & \(\surd\) & \(\surd\) \\ Scale 4 & & & \(\surd\) & \(\surd\) \\ DFPN & & & & \(\surd\) \\ Class 1 & 86.2 & 90.2 & 91.7 & 97.6 \\ Class 2 & 88.7 & 91.6 & 92.2 & 97.8 \\ Class 3 & 86.1 & 88.4 & 91.4 & 93.3 \\ Class 4 & 74.6 & 75.3 & 75.0 & 79.3 \\ mAP (\%) & 83.9 & 86.4 & 87.8 & 92.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiments on the RSOD datasets.