## 4 Experimental Results
### Ear Recognition Experiments of the Improved YOLOv5s-MG on Three Datasets
It is proved theoretically and experimentally that the human ear dataset with high resolution, a small amount of data, and relatively simple posture has a high ear recognition rate, that is, the [EMAIL_ADDRESS] value is large on the CCU-DE and USTB human ear datasets, and the [EMAIL_ADDRESS] value on the  
Figure 10: The [EMAIL_ADDRESS] value of YOLOv5s-MG (proposed), YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, YOLOv7, and YOLOv5s models trained on three human ear datasets: (**a**) CCU-DE ear dataset; (**b**) USTB ear dataset; (**c**) EarVN1.0 ear dataset (no convergence); (**d**) EarVN1.0 ear dataset (convergence). EarVN1.0 dataset is small. Third, it is related to the model. This could be expected as with the complexity reduction of the method, there is a decrease in precision. From Table 12 and Figure 11b-d, it can be seen that the params, GFLOPS, and model size of the improved YOLOv5s-MG model are much smaller than those of the other three lightweight models and YOLOv5s models, but the proposed model has slightly lower [EMAIL_ADDRESS] performance compared with the other methods for the observed dataset. On the CCU-DE ear dataset, the params, GFLOPS, and model size of the improved YOLOv5s-MG model are 30.37%, 22.56%, and 31.39% of YOLOv5s, respectively. On the USTB human ear dataset, the params, GFLOPS, and model size of the improved YOLOv5s-MG model are 31.88%, 24.85%, and 32.69% of YOLOv5s, respectively. On the EarVN1.0 ear dataset, the params, GFLOPS, and model size of the improved YOLOv5s-MG model are 31.88%, 24.85%, and 32.69% of YOLOv5s, respectively. On the EarVN1.0 ear dataset, the params, GFLOPS, and model size of the improved YOLOv5s-MG model are 30.47%, 22.56%, and 31.68% of YOLOv5s, respectively. From Table 12 and Figure 11, we can also see that the params, GFLOPS, and model size of YOLOv5s MG are much smaller than those of YOLOv7. Therefore, the experimental results show that in the three human ear datasets, the YOLOv5s-MG network is superior to the YOLOv5s-V3, YOLOv5s-V2, and YOLOv5s-G lightweight networks in reducing the number of parameters, computation, and model size on the basis of maintaining the recognition accuracy at the same level. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Human Ear Dataset** & **Model** & **Params (M)** & **GFLOPS (G)** & **Model Size (MB)** & **[EMAIL_ADDRESS]** \\ \hline \multirow{6}{*}{CCU-DE (epoch = 150)} & YOLOv5s & 6.75 & 16.4 & 13.7 & 0.999 \\ \cline{2-6}  & YOLOv5s-V3 [39] & 3.39 & 6.3 & 7.04 & 0.998 \\ \cline{2-6}  & YOLOv5s-V2 [40] & 3.63 & 8 & 7.56 & 0.999 \\ \cline{2-6}  & YOLOv5s-G [41] & 3.2 & 8.9 & 6.8 & 0.999 \\ \cline{2-6}  & YOLOv5s-MG & 2.05 & 3.7 & 4.3 & 0.997 \\ \cline{2-6}  & YOLOv7 & 34.84 & 103.5 & 71.41 & 0.993 \\ \hline \multirow{6}{*}{USTB (epoch = 150)} & YOLOv5s & 6.9 & 16.9 & 14.07 & 1 \\ \cline{2-6}  & YOLOv5s-V3 [39] & 3.55 & 6.8 & 7.37 & 1 \\ \cline{2-6}  & YOLOv5s-V2 [40] & 3.79 & 8.5 & 7.88 & 1 \\ \cline{2-6}  & YOLOv5s-G [41] & 3.4 & 9.4 & 7.14 & 1 \\ \cline{2-6}  & YOLOv5s-MG & 2.2 & 4.2 & 4.6 & 1 \\ \cline{2-6}  & YOLOv7 & 35.14 & 104.5 & 72.04 & 0.904 \\ \hline \multirow{6}{*}{EarVN1.0 (epoch = 150)} & YOLOv5s & 6.76 & 16.4 & 13.7 & 0.793 \\ \cline{2-6}  & YOLOv5s-V3 [39] & 3.4 & 6.4 & 7.05 & 0.389 \\ \cline{1-1} \cline{2-6}  & YOLOv5s-V2 [40] & 3.6 & 8 & 7.57 & 0.322 \\ \cline{1-1} \cline{2-6}  & YOLOv5s-G [41] & 3.2 & 8.9 & 6.82 & 0.543 \\ \cline{1-1} \cline{2-6}  & YOLOv5s-MG & 2.06 & 3.7 & 4.34 & 0.356 \\ \cline{1-1} \cline{2-6}  & YOLOv7 & 34.86 & 103.6 & 71.45 & 0.303 \\ \hline \multirow{6}{*}{EarVN1.0 (epoch = 1000)} & YOLOv5s & 6.76 & 16.4 & 13.7 & 0.882 \\ \cline{1-1} \cline{2-6}  & YOLOv5s-V3 [39] & 3.4 & 6.4 & 7.05 & 0.811 \\ \cline{1-1} \cline{2-6}  & YOLOv5s-V2 [40] & 3.6 & 8 & 7.57 & 0.818 \\ \cline{1-1} \cline{2-6}  & YOLOv5s-G [41] & 3.2 & 8.9 & 6.82 & 0.885 \\ \cline{1-1} \cline{2-6}  & YOLOv5s-MG & 2.06 & 3.7 & 4.34 & 0.855 \\ \cline{1-1} \cline{2-6}  & YOLOv7 & 34.86 & 103.6 & 71.45 & 0.869 \\ \hline \hline \end{tabular}
\end{table}