## 6 Conclusions  
In the task of object detection for UAV aerial imagery, several challenges arise, including a high proportion of small objects and large-scale variations. Many existing models struggle to achieve high detection accuracy while balancing computational resource consumption. This paper presents an enhanced YOLOv8 algorithm that integrates a recursive feature pyramid (RFP) structure, leading to the following conclusions:  
1. Conducting two rounds of \"processing\" for multi-scale detection enhances detection accuracy, particularly in situations where there is a large presence of small objects.
2. Leveraging fine-grained details effectively mitigates feature degradation during the convolution process, improving the model's attention to small objects.
3. By separating the aspect ratio component in the bounding box regression loss function, the process of regressing predicted bounding boxes is accelerated. Additionally, incorporating angle loss helps to avoid the \"wandering\" effect in predicted bounding boxes.  
The enhanced model results in an 8.2% and 3.4% improvement in mAP0.5 for the VisDrone-DET2021 and UAVDT datasets, respectively. However, the two-stage \"processing\" of the images makes the network architecture more intricate, leading to a rise in computational demands and inference time. The GFLOPs value for YOLOv8s is 28.5, while RPS-YOLO has a GFLOPs value of 37.7, suggesting potential for further optimization in the use of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Baseline** & **RFP** & **SCC2f** & **LIOU** & **mAP0.5/\%** & **mAP0.5:0.95/\%** & **Params(M)** & **GFLOPs** \\ \hline \multirow{4}{*}{YOLOv8s} & \multirow{4}{*}{\(\surd\)} & & & 38.1 & 22.7 & 11.1 & 28.5 \\  & & & & 44.0 & 26.0 & 12.7 & 36.7 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & & 45.9 & 27.5 & 13.3 & 37.7 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & \(\surd\) & 46.3 & 28.1 & 13.3 & 37.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RPS-YOLO ablation experiment results on the VisDrone-DET2021 validation dataset.  
computational resources. Future studies will focus on boosting the model's detection precision while also enhancing computational efficiency and reducing inference time.  
Conceptualization, P.L. ([PERSON]); data curation, C.W.; formal analysis, P.L. ([PERSON]) and C.W.; funding acquisition, P.L. ([PERSON]); investigation, C.W.; methodology, P.L. ([PERSON]); project administration, P.L. ([PERSON]); resources, P.L. ([PERSON]); software, P.L. ([PERSON]); supervision, P.L. ([PERSON]); validation, C.W.; writing--original draft, P.L. ([PERSON]); writing--review and editing, P.L. ([PERSON]) and P.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the National Key R&D Program of China (grant no. 2019 YFF0301800) and the National Natural Science Foundation of China (grant no. 61379106).  
The data that support the findings of this study are available in VisDrone-DET2021 at [[https://doi.org/10.1109/ICCVW54120.2021.00319](https://doi.org/10.1109/ICCVW54120.2021.00319)]([https://doi.org/10.1109/ICCVW54120.2021.00319](https://doi.org/10.1109/ICCVW54120.2021.00319)) and UAVDT at [[https://doi.org/10.1007/s11263-019-01266-1](https://doi.org/10.1007/s11263-019-01266-1)]([https://doi.org/10.1007/s11263-019-01266-1](https://doi.org/10.1007/s11263-019-01266-1)).  
The authors declare no conflicts of interest.