## 4 Experimental Results
### Ear Recognition Experiments of the Improved YOLOv5s-MG on Three Datasets  
In order to verify the feasibility and effectiveness of the proposed lightweight network YOLOv5s-MG in this paper and to verify the similarities and differences between the improved lightweight network, the original YOLOv5s model, and the YOLOv7 model, we trained and tested YOLOv5s-MG, YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, YOLOv7, and YOLOv5s networks with the same parameters on CCU-DE, USTB, and EarVN1.0 human ear datasets. The experimental platform and experimental parameters are shown in Tables 9 and 10. Figure 10 shows the training curves of YOLOv5s-MG, YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, YOLOv7, and YOLOv5s networks using the training set and validation set on three datasets. The abscissa is the training epoch, and the ordinate is the accuracy ([EMAIL_ADDRESS]). It can be seen from Figure 10 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 ear dataset is about 40, as shown in Figure 10a. The epoch of model convergence on the USTB ear dataset is about 50, as shown in Figure 10b. The convergence time epoch of the model on the EarVN1.0 ear dataset is about 500, as shown in Figure 10d. Figure 10c shows the case of the model not converging when epoch = 150 on the EarVN1.0 ear dataset. Among the four lightweight models, the YOLOv5s-MG model converges faster and the curve is smooth, while YOLOv5s-V2 converges slower. Among the six methods, the YOLOv7 model has the slowest convergence speed and more oscillations during training. In this experiment, epoch = 150 and epoch = 1000 were selected as quantities to verify the difference between the four lightweight YOLOv5s models and the YOLOv5s model. The experimental results on the testing set of the three human ear datasets are shown in Table 12 and Figure 11, respectively. From Table 12, we can see that the difference in [EMAIL_ADDRESS] values is mainly related to three factors. First, it is related to whether the model converges. It can be seen from Figure 10 that the six models on the CCU-DE and USTB human ear datasets are completely convergent in the case of 150 epochs, so the accuracy of human ear recognition is high, that is, the [EMAIL_ADDRESS] value is large. However, on the EarVN1.0 dataset, when the epoch is 150, the model begins to converge. Because the models have not yet fully converged, the [EMAIL_ADDRESS] value will be small. Compared with epoch = 150 and epoch = 1000 on the EarVN1.0 ear dataset in Figures 10c,d and 11a, and Table 12, [EMAIL_ADDRESS] of all methods increased, and especially in the four lightweight methods. [EMAIL_ADDRESS] increased greatly. [EMAIL_ADDRESS] of the YOLOv5s-V3 method increased by 42.2% from 0.389 to 0.811. [EMAIL_ADDRESS] of the YOLOv5s-V2 method increased by 49.6% from 0.322 to 0.818. [EMAIL_ADDRESS] of the YOLOv5s-G method increased by 34.2% from 0.543 to 0.885. [EMAIL_ADDRESS] of the YOLOv5s-MG (the proposed) method increased by 49.9% from 0.356 to 0.855. [EMAIL_ADDRESS] of the YOLOv5s method increased by 8.9% from 0.793 to 0.882. Second, it is related to the human ear dataset. From Table 8, the three ear datasets of CCU-DE, USTB, and EarVN1.0 have their own characteristics. From the experimental results shown in Figures 10d and 11a, and Table 12, the six networks except YOLOv7 have the highest ear recognition rate on the USTB ear dataset, and the [EMAIL_ADDRESS] value is 1. CCU-DE follows, and [EMAIL_ADDRESS] is above 0.99. On the EarVN1.0 dataset, the human ear recognition rate is the lowest, and the [EMAIL_ADDRESS] value is the smallest. When epoch = 1000, [EMAIL_ADDRESS] is above 0.81.