## 3 Results  
### Optimization of the Experimental Parameters  
In this paper, different activation functions, initial learning rates, and batch sizes were tested for 10,000 iterations via comparative experiments to evaluate their influence on the accuracy of the proposed ResNet-Locust-BN model. This is to get insight on how to optimize the settings of the model parameters. Stochastic gradient descent optimizer was used with default parameters (momentum = 0, decay = 0.0) in the experiments conducted. First, model accuracy and loss ration under the Sigmod, Tanh, and ReLU activation functions were calculated and listed based on the training and validation datasets as shown in Figure 5. Initial learning rate and batch size were set as 0.01 and 16, respectively. The accuracy (acc) is calculated as the ratio of the number of correctly identified samples to the total number of samples in the training dataset. The validation_accuracy (val acc) is calculated as the ratio of the total number of correctly identified samples to the total of samples in the validation dataset. The loss and val_loss refer to loss ratio of training and validation datasets, respectively. As the experimental result shows, the suitability of the Sigmod activation function is significantly lower than other activation functions as it resulted in lower acc, val_acc, and slower convergence rate of val_loss. ReLU activation function produced higher and more stable acc than Tanh. The val_acc of ReLU is similar to that of Tanh, but it exhibited better stability. Although loss obtained in the case of ReLU shows a faster convergence rate than that of Tanh, the change characteristics of its val_loss is similar to that of Tanh function. Hence, we selected ReLU as the model activation function as it exhibited better accuracy and stability. Figure 5: Comparison of the accuracy rate and the loss rate of ResNet-Locust-BN based on different activation functions (initial learning rate = 0.01, batch size = 16). Further, the advantages of introducing the batch normalization method [38] are to make the model compatible with larger initial vectors and to speed up the model convergence theoretically. In this study, we compared three different initial vector loss values, 0.001, 0.01, and 0.1, to improve the model accuracy, as shown in Figure 6. Batch size and activation function were set as 16 and ReLU, respectively. The experimental results show that increasing the initial learning rate from 0.01 to 0.1 significantly accelerated the convergence rate of both loss and val_loss. The small change of initial learning rate (i.e., from 0.001 to 0.01) has slight influence on the acc and val_acc. It is worthy of note that the acc and val_acc significantly improved as the initial learning rate was adjusted from 0.01 to 0.1. Furthermore, no overfitting phenomenon occurred during 10,000 iterations in these experiments by applying the batch normalization method. Hence, it is appropriate to set an initial learning rate as 0.1, by which both the accuracy rate and the loss rate can be improved. Finally, we set the batch size to 8, 16, and 32 and calculated the changes in the recognition accuracy and the loss rate under corresponding conditions. As shown in Figure 7, change of batch size has little influence on the final identification accuracy of the model after thousands of iterations. However, as the batch size increases from 8 to 32, the fluctuation intensity decreased and the convergence rate of accuracy (acc and val_acc) increases. According to the experimental results we obtained, when the  
Figure 6: Comparison of the accuracy rate and the loss rate of ResNet-Locust-BN based on different initial learning rates (activation function = ReLU, batch size = 16). batch size is 32, the recognition accuracy is the highest and most stable with the fastest convergence rate. Because excessive batch size may reduce the generalization ability of the model and limited by the hardware performance, the optimal batch size was set as 32.