# Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
## 4 Experimental Results and Analysis
### Ear Recognition Experiments of the Improved YOLOv5s-MG-CBAM-F on Three Datasets  
In order to verify the feasibility and effectiveness of the YOLOv5s-MG-CBAM-F based on feature fusion proposed in this paper, and verify the similarities and differences between YOLOv5s-MG-CBAM-F, YOLOv5s-MG-SE-F, YOLOv5s-MG-ECA-F, YOLOv5s-MG-CA-F and the original YOLOv5s model and YOLOv5s-MG, the six networks with the same parameters on CCU-DE, USTB and EarVN1.0 human ear datasets were trained and tested. The six methods YOLOv5s-MG-CBAM-F (proposed), YOLOv5s-MG, YOLOv5s-MG-SE-F, YOLOv5s-MG-CA-F, YOLOv5s-MG-ECA-F and YOLOv5s are tested by using test sets on three human ear datasets, and the training curves are shown in Figure 7. It can be  
Figure 6: The comparison results of YOLOv5s-MG-CBAM (proposed), YOLOv5s-MG, YOLOv5s-MG-SE, YOLOv5s-MG-CA, YOLOv5s-MG-ECA and YOLOv5s models on three datasets. (**a**) [EMAIL_ADDRESS]; (**b**) params; (**c**) GFLOPS; (**d**) the model size. seen from Figure 7 that the value of [EMAIL_ADDRESS] gradually tends to be stable and the model converges when epoch increases. The epoch of the model convergence is about 40, 50 and 500 on the CCU-DE, USTB and EarVN1.0 human ear dataset respectively. This is mainly related to the difference pose, resolution, image size and number of each human ear dataset. From Figure 7, it can be found that compared with YOLOv5s-MG-SE-F, YOLOv5s-MG-ECA-F and YOLOv5s-MG-CA-F, YOLOv5s-MG-CBAM-F has the fastest convergence speed on the three human ear datasets. In this experiment, epoch = 150 and epoch = 1000 were selected as quantities to verify the difference between the six models. The experimental results on testing a set of the three human ear datasets are shown in Table 2 and Figure 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ear Dataset** & **Model** & **Params (M)** & **GFLOPS (G)** & **Model Size (MB)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline \multirow{6}{*}{(epoch = 150)} & YOLOv5s [17] & 6.75 [17] & 16.4 [17] & 13.7 [17] & 0.999 [17] & 0.826 \\  & YOLOv5s-MG [17] & 2.05 [17] & 3.7 [17] & 4.3 [17] & 0.997 [17] & 0.771 \\  & YOLOv5s-MG-CEAM-F & 5.3 & 8 & 10.3 & 0.986 & 0.781 \\  & YOLOv5s-MG-SE-F & 2.87 & 4.6 & 6.02 & 0.988 & 0.766 \\  & YOLOv5s-MG-ECA-F & 2.87 & 4.6 & 6.02 & 0.988 & 0.777 \\  & YOLOv5s-MG-CA-F & 2.8 & 4.5 & 5.9 & 0.986 & 0.771 \\ \hline \multirow{6}{*}{(epoch = 150)} & YOLOv5s [17] & 6.9 [17] & 16.9 [17] & 14 [17] & 1 [17] & 0.918 \\  & YOLOv5s-MG [17] & 2.2 [17] & 4.2 [17] & 4.6 [17] & 1 [17] & 0.903 \\ \cline{1-1}  & YOLOv5s-MG-CBAM-F & 5.89 & 8.8 & 12.08 & 1 & 0.906 \\ \cline{1-1}  & YOLOv5s-MG-SE-F & 3.03 & 5.1 & 6.23 & 1 & 0.903 \\ \cline{1-1}  & YOLOv5s-MG-ECA-F & 3.03 & 5.1 & 6.35 & 1 & 0.904 \\ \cline{1-1}  & YOLOv5s-MG-CA-F & 2.97 & 5 & 6.23 & 1 & 0.906 \\ \hline \multirow{6}{*}{(epoch = 100)} & YOLOv5s [17] & 6.76 [17] & 16.4 [17] & 13.7 [17] & 0.793 [17] & 0.635 \\  & YOLOv5s-MG [17] & 2.06 [17] & 3.7 [17] & 4.34 [17] & 0.356 [17] & 0.281 \\ \cline{1-1}  & YOLOv5s-MG-CBAM-F & 5.2 & 8.3 & 10.9 & 0.698 & 0.559 \\ \cline{1-1}  & YOLOv5s-MG-SE-F & 2.89 & 4.6 & 6.03 & 0.502 & 0.398 \\ \cline{1-1}  & YOLOv5s-MG-CEA-F & 2.89 & 4.6 & 6.03 & 0.518 & 0.407 \\ \cline{1-1}  & YOLOv5s-MG-CA-F & 2.8 & 4.5 & 5.9 & 0.518 & 0.414 \\ \hline \multirow{6}{*}{(epoch = 100)} & YOLOv5s [17] & 6.76 [17] & 16.4 [17] & 13.7 [17] & 0.882 [17] & 0.712 \\ \cline{1-1}  & YOLOv5s-MG [17] & 2.06 [17] & 3.7 [17] & 4.34 [17] & 0.855 [17] & 0.706 \\ \cline{1-1}  & YOLOv5s-MG-CBAM-F & 5.2 & 8.3 & 10.9 & 0.919 & 0.755 \\ \cline{1-1}  & YOLOv5s-MG-SE-F & 2.89 & 4.6 & 6.03 & 0.856 & 0.702 \\ \cline{1-1}  & YOLOv5s-MG-ECA-F & 2.89 & 4.6 & 6.03 & 0.878 & 0.718 \\ \cline{1-1}  & YOLOv5s-MG-CA-F & 2.8 & 4.5 & 5.9 & 0.870 & 0.713 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison results of the improved YOLOv5s-MG-CBAM-F and other methods.