3.1.2. Comparison of Different Backbone Networks

Under the network configuration parameters of Test 10, a comparative test was conducted for different backbone networks to demonstrate the advantages of HDC-Net. The neural networks of HDC-Net, ResNet50, Res-Net101 and MobileNetV1 were all composed of residual blocks, which simplified their architectures with residual learning, reduced their computational overhead and well solved the gradient vanishing problem.

Its performance was compared in four aspects. Network training time, image detection time per second, network model weight and accuracy (S > 90 means that SMask is greater than 90). Accuracy is the ratio of high-quality labels to all labels. It can be seen from the Table 4 that when HDC-Net is used as the backbone network, the training time is 13.21 h, which is quite similar to ResNet50; the speeds of these four networks are 6.65 sheets per second, 6.25 sheets per second, 4.6 sheets per second and 5.2 sheets per second respectively, and HDC-Net has the fastest speed for calibrating the image. In the model size comparison test, when HDC-Net is used as the backbone network, the label model size is the smallest. When HDC-Net, ResNe50, ResNet101 and MobileNet V1 are used as the backbone network, the accuracy of the vehicle image label is 95.1%, 93.4%, 93.8% and 84.5%, respectively. It can be seen that although HDC-Net has a slight increase in training time compared with ResNet50, it is far ahead of other backbone networks in terms of speed, model weight and accuracy. Therefore, HDC-Net has the best performance.

**Table 4.** Performance comparison of four backbone networks.

