## 3 Results and Discussion
### Experimental Results and Analysis
These data underscore the practical applicability of RGSDD, as shown in Figure 15. Specifically, in the case of the YOLOv8 model with 30% incorporation, it yielded a substantial 3.79% improvement in mAP. Similarly, for the DETR model with 40% incorporation, there was a noticeable enhancement of 4.36% in mAP. Furthermore, it is evident that when 50% of the generated data are included, the model's performance experiences a significant decline. This subset of data appears to introduce interference and is potentially treated as noise to some extent, resulting in adverse effects on model performance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **BA** & **HA** & **MP** & **SE** & **SL** & **TP** & **TU** & **ZC** \\ \hline SSD & 77.29 & 73.12 & 77.48 & 73.88 & 79.91 & 80.21 & 78.26 & 74.08 \\ Faster R-CNN & 75.89 & 69.26 & 69.76 & 73.81 & 71.33 & 74.75 & 70.10 & 73.02 \\ YOLOv3 & 57.20 & 63.69 & 61.51 & 60.66 & 62.63 & 58.93 & 58.00 & 64.05 \\ YOLOv4 & 72.55 & 74.47 & 75.40 & 79.11 & 74.24 & 76.13 & 80.05 & 78.51 \\ YOLOv5m & 84.22 & 79.51 & 77.17 & 79.57 & 80.79 & 79.73 & 83.06 & 81.16 \\ YOLOv8 & 81.53 & 88.45 & 82.18 & 84.44 & 85.56 & 84.73 & 83.95 & 83.21 \\ DETR & 83.53 & 82.07 & 87.33 & 85.61 & 87.62 & 83.23 & 88.52 & 85.52 \\ RS Transformer & 91.33 & 91.46 & 88.83 & 86.21 & 92.63 & 89.44 & 87.74 & 91.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different mAP indexes. Figure 14: Mixed data model diagram. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Percentage** & **mAP (\%)** & **F1 Score (\%)** & **Recall (\%)** & **mDT (ms)** \\ \hline \multirow{8}{*}{YOLOv8} & 0\% & 84.72 & 80.32 & 82.11 & 9.8 \\  & 10\% & 87.38 & 75.77 & 72.31 & 9.8 \\  & 20\% & 88.42 & 85.17 & 84.78 & 9.8 \\ \cline{1-1}  & 30\% & 88.51 & 85.89 & 85.31 & 9.8 \\ \cline{1-1}  & 40\% & 82.32 & 81.76 & 80.11 & 9.9 \\ \cline{1-1}  & 50\% & 75.35 & 70.32 & 71.58 & 9.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: RGSDD using DETR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Percentage** & **mAP (\%)** & **F1 Score (\%)** & **Recall (\%)** & **mDT (ms)** \\ \hline \multirow{8}{*}{Faster R-CNN} & 0\% & 72.65 & 65.57 & 69.31 & 24 \\  & 10\% & 75.07 & 68.83 & 69.73 & 24 \\  & 20\% & 73.47 & 67.26 & 70.62 & 24 \\ \cline{1-1}  & 30\% & 73.72 & 67.37 & 74.84 & 24 \\ \cline{1-1}  & 40\% & 71.80 & 69.78 & 72.39 & 24.1 \\ \cline{1-1}  & 50\% & 73.13 & 68.29 & 70.47 & 24.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: RGSDD using Faster R-CNN. Figure 15: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Percentage** & **mAP (\%)** & **F1 Score (\%)** & **Recall (\%)** & **mDT (ms)** \\ \hline \multirow{8}{*}{YOLOv8} & 0\% & 85.56 & 81.18 & 82.82 & 20.1 \\  & 10\% & 85.94 & 83.10 & 80.62 & 20.1 \\ \cline{1-1}  & 20\% & 86.37 & 82.99 & 84.67 & 20.1 \\ \cline{1-1}  & 30\% & 87.71 & 86.75 & 85.72 & 20.2 \\ \cline{1-1}  & 40\% & 89.92 & 85.02 & 87.89 & 20.2 \\ \cline{1-1}  & 50\% & 88.90 & 87.19 & 85.97 & 20.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: RGSDD using YOLOv8. Comparing the mAP, F1 score, and recall of different networks, it can be seen that RS Transformer is still better than the others, even when the RGSDD is used. At the optimal value, mAP outperforms Faster R-CNN by 9.29% and YOLOv5m by 4.95%. Figure 16 presents the outcomes achieved by the RS Transformer model integrated with the RGSDD. Notably, the results highlight the RGSDD's exceptional accuracy in effectively identifying multi-scale pests across various species.