## 4 Experiments
### Experiment Results
#### 4.4.2 Comparison with Lightweight Networks  
To assess the performance improvements of the proposed network, we performed comparative experiments between several prominent lightweight networks and the RPS-YOLO network using the VisDrone-DET2021 and UAVDT datasets. The results of the experiments on the VisDrone-DET2021 validation dataset are presented in Table 2.  
The best result is presented in bold.  
Based on the experimental results in Table 2, it is evident that while YOLOv3-Tiny offers a lightweight model, it significantly sacrifices detection accuracy. Compared to the YOLOv6s model, YOLOv5s, YOLOv8s, and YOLOv10s exhibit better detection performance with fewer parameters. However, these models all utilize a three-scale detection structure, which does not fully address the detection challenges posed by high proportions of small objects. Consequently, despite only minor differences in parameters, the approach introduced in this study achieves superior accuracy in object detection. However, due to the two-stage image \"processing\" involved, the model incurs a higher computational cost and has the lowest FPS. PP-PicoDet-L, despite having the smallest parameter count, exhibits relatively lower detection accuracy. In conclusion, RPS-YOLO outperforms the other models, achieving the highest average detection accuracy and overall detection performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **mAP0.5 (\%)** & **mAP0.5:0.95 (\%)** & **Params (M)** & **GFLOPs** \\ \hline Faster R-CNN [7] & 35.7 & 20.1 & 41.2 & 268.5 \\ TOOD [36] & 41.0 & - & 31.81 & 144.40 \\ SABL [37] & 40.5 & 25.9 & 99.6 & 145.5 \\ RT-DETR [38] & 44.1 & 26.9 & 32.0 & 103.5 \\ RSDD [39] & 43.3 & - & 63.72 & 84.21 \\ Drone-Yolo-m [40] & **48.6** & - & 33.9 & - \\ CenterNet [41] & 32.7 & 18.0 & 32.7 & 109.3 \\
**RPS-YOLO** & 46.3 & **28.1** & **13.3** & **37.7** \\ \hline \hline \end{tabular}  
* The best result is presented in bold.  
\end{table}
Table 1: Comparison experiment results of large-scale networks and RPS-YOLO on the VisDrone-DET2021 validation dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Network** & **Precision/\%** & **Recall/\%** & **mAP0.5 (\%)** & **mAP0.5:0.95 (\%)** & **Params (M)** & **GFLOPs** & **FPS** \\ \hline YOLOv3-tiny [9] & 38.1 & 24.3 & 23.5 & 13 & 12.1 & 18.9 & **86** \\ YOLOv5s & 49.6 & 36.7 & 37.5 & 22.4 & 9.1 & 23.8 & 60 \\ YOLOv6s [11] & 47.8 & 35.5 & 36.4 & 21.9 & 16.3 & 44.0 & 52 \\ YOLOv8s & 50.6 & 37.2 & 38.1 & 22.7 & 11.1 & 28.5 & 56 \\ PP-PicoDet-L [42] & - & - & 34.2 & - & **3.30** & **8.9** & 67 \\ YOLOv10s [15] & 48.9 & 37.4 & 37.9 & 22.7 & 8.1 & 24.5 & 50 \\
**RPS-YOLO** & **55.3** & **44.3** & **46.3** & **28.1** & 13.3 & 37.7 & 49 \\ \hline \hline \end{tabular}  
* The best result is presented in bold.  
\end{table}
Table 2: Comparison experiment results of lightweight and RPS-YOLO networks on the VisDrone-DET2021 validation dataset.  
The results of the experiments on the UAVDT dataset are presented in Table 3. As observed, the proposed RPS-YOLO network outperforms the YOLOv8s network by achieving a 3.4% improvement in mAP0.5, highlighting the competitive edge of the proposed model.