*5.1. Comparison of Popular Network Models*

To further validate the efficiency of the proposed method, the test results of MobileNetV2 model are compared with those of AlexNet, VGG16, and ResNet. Specifically, the above network models are transferred to identification tasks in the same fine-tuning form as the MobileNetV2 model. The acceleration samples are used as input on vehicle speed identification task, and the displacement samples are used as input on vehicle weight identification task. To compare the computational efficiency of the models, the training time

complete 50 epochs, the weight file of model generated after identification of 50 samples in the test set, and the identification time for each model are compared.

The training results for vehicle weights identification task are shown in Figure 12, After 50 epochs, the results of the identification of the test set are shown in Tables 7 and 8. As can be seen from Figure 12, the MobileNetV2 model and each of the other models have converged after 50 epochs. As can be seen from Table 7, the final accuracy of the training set is above 98% for MobileNetV2 and other models. For the final accuracy of the validation set, VGG16 model has the highest accuracy of 100%, AlexNet has the lowest accuracy of 94%, and MobileNetV2 and ResNet both reach 98%. For training time, MobileNetV2 takes 53 min to train, which is only 10% of VGG16 and 55% of AlexNet and ResNet. In terms of identification speed, MobileNetV2 took 11.04 s to identify 50 test samples, which was only 27.8% of VGG16, 44.3% of AlexNet, and 58.8% of ResNet.

**Figure 12.** Results of vehicle weights identification of different models: (**a**) Training accuracy curves of response samples; (**b**) Training loss curves of the response samples; (**c**) Validation accuracy curves of the response samples; (**d**) Validation loss curves of response samples.

**Table 7.** Train results of vehicle weight identification of different models after 50 epochs.



**Table 8.** Identification results of vehicle weight for different models.

Training results of vehicle speed identification task are shown in Figure 13. The identification results of the test set after 50 epochs are shown in Tables 9 and 10. From Figure 13, it can be seen that the MobileNetV2 model and each of the other models have converged after 50 epochs, On the accuracy curves of the training and validation sets, ResNet and MobileNetV2 rise faster and fluctuate less, while AlexNet and VGG16 rise slower and fluctuate more. The accuracy of both the training and validation sets after training is 100% for MobileNetV2 and 98% for all the other three models. Based on the identification results in Table 10, MobileNetV2 and VGG16 have the highest accuracy of 100%, while AlexNet has the lowest accuracy of 94%. The weight file size of MobileNetV2 is only 1.7% of VGG16, 3.9% of AlexNet, and 20% of ResNet, which has obvious advantages. In terms of identification speed, MobileNetV2 takes 9.58S to identify 50 test samples, which is only 24.8% of VGG16, 49.7% of AlexNet, and 50% of ResNet.

**Figure 13.** Results of vehicle speed identification of different models: (**a**) Training accuracy curves of response samples; (**b**) Training loss curves of the response samples; (**c**) Validation accuracy curves of the response samples; (**d**) Validation loss curves of response samples.


**Table 9.** Train results for vehicle speed identification of different models after 50 epochs.

**Table 10.** Identification results of vehicle speed for different models.


In summary, MobileNetV2 outperforms the other three models in both vehicle weight identification and vehicle speed identification tasks, in terms of identification accuracy. At the same time, MobileNetV2 is superior to other models in terms of training time and memory resource occupancy on the premise of ensuring accuracy. Additionally, MobileNetV2 has a major advantage in the speed of identification from the test set. It can be seen that this method has the best performance in terms of identification speed and accuracy, and the short training time and identification time make this method more suitable for practical applications.
