## 4 Experimental Results
### Ear Recognition Experiments of Four YOLOv5 Models on Three Datasets  
In this experiment, four YOLOv5 models--YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x--were used to conduct experiments on CCU-DE, USTB, and EarVN1.0 human ear datasets. Figure 8 is the training curve of different versions of the YOLOv5 network model using the training set and validation set in three datasets, the abscissa is the training epoch, and the ordinate is the accuracy ([EMAIL_ADDRESS]). It can be seen from Figure 8 that as the number of epochs gradually increases, the value of [EMAIL_ADDRESS] gradually tends to be stable, that is, the model converges. However, for different human ear datasets, the epoch of convergence is different because of the difference in the pose, resolution, image size, and number. The epoch of model convergence on the CCU-DE human ear dataset is about 40, the epoch of model convergence on the USTB human ear dataset is about 50, and the epoch of model convergence on the EarVN1.0 human ear dataset is about 150. In this experiment, epoch 150 was selected as a quantitative to verify the difference between different versions of YOLOv5. When the epoch value is 150, the experimental results of the four evaluation indicators on the testing set of the three human ear datasets are shown in Table 11 and Figure 9. Figure 9 is the accuracy ([EMAIL_ADDRESS]) comparison result, Figure 9 is the parameter quantity (params) comparison result, Figure 9 is the calculation amount (GFLOPS) comparison result, and Figure 9 is the model size comparison result. Figure 8: The [EMAIL_ADDRESS] value of four YOLOv5 models trained on three human ear datasets: (a) CCU-DE ear dataset; (b) USTB ear dataset; (c) EarVN1.0 ear dataset. From Table 11 and Figure 9a, it can be seen that on the USTB ear dataset, the four YOLOv5 models have the highest ear recognition rate: the [EMAIL_ADDRESS] value is 1, CCU-DE follows, and [EMAIL_ADDRESS] is above 0.99; on the EarVN1.0 dataset, the [EMAIL_ADDRESS] values of the four YOLOv5 models vary greatly, with the highest value above 0.9 and the lowest value above 0.7. \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{4}{*}{CCU-DE (epoch = 150)} & YOLOv5s & 6.75 & 16.4 & 13.7 & 0.999 \\ \cline{2-6}  & YOLOv5m & 20.1 & 50.4 & 40.55 & 0.997 \\ \cline{2-6}  & YOLOv5l & 44.49 & 114.2 & 89.5 & 0.998 \\ \cline{2-6}  & YOLOv5x & 83.22 & 217.3 & 167 & 0.999 \\ \hline \multirow{4}{*}{USTB (epoch = 150)} & YOLOv5s & 6.9 & 16.9 & 14.07 & 1 \\ \cline{2-6}  & YOLOv5m & 20.32 & 51.2 & 41.03 & 1 \\ \cline{2-6}  & YOLOv5l & 44.79 & 115.3 & 90.09 & 1 \\ \cline{2-6}  & YOLOv5x & 83.60 & 218.6 & 167.85 & 1 \\ \hline \multirow{4}{*}{EarVN1.0 (epoch = 150)} & YOLOv5s & 6.76 & 16.4 & 13.7 & 0.793 \\ \cline{2-6}  & YOLOv5m & 20.12 & 50.5 & 40.5 & 0.723 \\ \cline{1-1} \cline{2-6}  & YOLOv5l & 44.51 & 114.3 & 89.5 & 0.947 \\ \cline{1-1} \cline{2-6}  & YOLOv5x & 83.25 & 217.4 & 167.1 & 0.965 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Quantitative comparison results of YOLOv5s, YOLOv5m, YOLOv5m, and YOLOv5x. Figure 9: The comparison results of four evaluation indexes of five methods on three datasets: (**a**) [EMAIL_ADDRESS]; (**b**) the parameter quantity (params); (**c**) the computation amount (GFLOPS); and (**d**) the model size. The difference in [EMAIL_ADDRESS] values is mainly related to three factors. First, related to the model, the YOLOv5x model has the largest depth and width, and the strongest feature extraction ability, so the [EMAIL_ADDRESS] value is the largest. The YOLOv5s model has the smallest depth and width, and the weakest feature extraction ability, so the [EMAIL_ADDRESS] value is the smallest. Second, it is related to whether the model converges. It can be seen from Figure 8 that the four models on the CCU-DE and USTB human ear datasets are completely convergent, so the accuracy of human ear recognition is high, that is, the [EMAIL_ADDRESS] value is large.