*3.2. Model Initialization*

In the process of network training, in order to improve efficiency and better save computing resources and time, this paper adopts the training strategy of freezing certain layers. The entire training process is divided into two stages. In the first stage, only the backbone network structure is trained; in the second stage, the overall network structure is trained. In the training process, the Cosine Annealing learning rate strategy is adopted, and the hyperparameters are optimized according to the genetic algorithm. The initial parameter settings of the first stage and the second stage are shown in Tables 1 and 2, respectively.

**Table 1.** Initial parameters of the first stage of the training process.


**Table 2.** Initial parameters of the second stage of the training process.

