# 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 on Three Datasets  
In order to test the accuracy of ear recognition of the YOLOv5s-MG-CBAM method based on the attention mechanism and the YOLOv5s-MG method proposed in this paper, and verify the similarities and differences between the four lightweight networks based on the attention mechanism YOLOv5s-MG-CBAM, YOLOv5s-MG-SE, YOLOv5s-MG-ECA, YOLOv5s-MG-CA 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. Figure 5 is the training curves of YOLOv5s-MG-CBAM (proposed), YOLOv5s-MG, YOLOv5s-MG-SE, YOLOv5s-MG-CA, YOLOv5s-MG-ECA and YOLOv5s networks by using the training set and validation set in three human ear datasets. The abscissa and the ordinate are the training epoch and [EMAIL_ADDRESS], respectively. It can be seen from Figure 5 when the number of epochs gradually increases, the model tends to converge gradually. However, the epoch of the model convergence on the CCU-DE, USTB and EarVN1.0 ear dataset is 40, 50 and 500, respectively. As shown in Figure 5, compared with YOLOv5s-MG-SE, YOLOv5s-MG-ECA and YOLOv5s-MG-CA, YOLOv5s-MG-CQAM converges fastest on the EarVN1.0 human ear dataset, and converges slowly on the CCU-DE and USTB human ear datasets. This is mainly because the YOLOv5s-MG-CQAM method is mainly designed for the poor image quality and low resolution of the EarVN1.0 human ear dataset, while the image quality of CCU-DE and USTB human ear datasets is good and overfitting occurs when training with higher resolution. In this experiment, epoch = 150 and epoch = 1000 were chosen to quantitatively test the difference between the six models. Table 1 and Figure 6 are the experimental results of the six methods on the test sets of three human ear datasets. \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 [17] & 6.75 [17] & 16.4 [17] & 13.7 [17] & 0.999 [17] \\  & YOLOv5s-MG [17] & 2.05 [17] & 3.7 [17] & 4.3 [17] & 0.997 [17] \\  & YOLOv5s-MG-CQAM & 3.28 & 6.1 & 6.86 & 0.986 \\  & YOLOv5s-MG-SE & 1.68 & 3.5 & 3.62 & 0.984 \\  & YOLOv5s-MG-ECA & 1.63 & 3.5 & 3.52 & 0.985 \\  & YOLOv5s-MG-CA & 1.62 & 3.4 & 3.5 & 0.987 \\ \hline \multirow{6}{*}{USTB (epoch = 150)} & YOLOv5s [17] & 6.9 [17] & 16.9 [17] & 14 [17] & 1 [17] \\  & YOLOv5s-MG [17] & 2.2 [17] & 4.2 [17] & 4.6 [17] & 1 [17] \\ \cline{1-1}  & YOLOv5s-MG-CQAM & 3.44 & 6.6 & 7.5 & 1 \\ \cline{1-1}  & YOLOv5s-MG-SE & 1.83 & 4.0 & 3.9 & 1 \\ \cline{1-1}  & YOLOv5s-MG-ECA & 1.786 & 4.2 & 3.84 & 1 \\ \cline{1-1}  & YOLOv5s-MG-CA & 1.786 & 3.9 & 3.9 & 1 \\ \hline \multirow{6}{*}{EarVN1.0 (epoch = 1000)} & YOLOv5s [17] & 6.76 [17] & 16.4 [17] & 13.7 [17] & 0.793 [17] \\  & YOLOv5s-MG-CQAM & 2.06 [17] & 3.7 [17] & 4.34 [17] & 0.356 [17] \\ \cline{1-1}  & YOLOv5s-MG-CSAM & 3.95 & 6.7 & 8.1 & 0.542 \\ \cline{1-1}  & YOLOv5s-MG-SE & 2.12 & 3.9 & 4.4 & 0.41 \\ \cline{1-1}  & YOLOv5s-MG-ECA & 2.02 & 3.8 & 4.3 & 0.379 \\ \cline{1-1}  & YOLOv5s-MG-CA & 2.06 & 3.8 & 4.2 & 0.355 \\ \hline \multirow{6}{*}{EarVN1.0 (epoch = 1000)} & YOLOv5s [17] & 6.76 [17] & 16.4 [17] & 13.7 [17] & 0.882 [17] \\  & YOLOv5s-MG [17] & 2.06 [17] & 3.7 [17] & 4.34 [17] & 0.855 [17] \\ \cline{1-1}  & YOLOv5s-MG-CBAM & 3.95 & 6.7 & 8.1 & 0.883 \\ \cline{1-1}  & YOLOv5s-MG-SE & 2.12 & 3.9 & 4.4 & 0.859 \\ \cline{1-1}  & YOLOv5s-MG-ECA & 2.02 & 3.8 & 4.3 & 0.833 \\ \cline{1-1}  & YOLOv5s-MG-CA & 2.06 & 3.8 & 4.2 & 0.855 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison results of the improved YOLOv5s-MG-CBAM and other methods. Figure 5: The [EMAIL_ADDRESS] value of YOLOv5s-MG-CBAM (proposed), YOLOv5s-MG, YOLOv5s-MG-SE, YOLOv5s-MG-CA, YOLOv5s-MG-ECA and YOLOv5s models trained on three human ear datasets. (a) CCU-DE ear dataset; (b) USTB ear dataset; (c) EarVN1.0 ear dataset.