## 4 Experiments and Analysis
### Dataset  
The dataset used includes real sonar images collected by lake and sea trials and simulated images obtained by three-dimensional modeling software, including sphere, cylinder, and truncated cone. The specific number of which is shown in Table 3. Simulated images are used for auxiliary training of the model. During the simulation, the grazing angle between sonar and the object is 30-45\({}^{\circ}\), and the angle between the object axis and the incident acoustic wave is 0-180\({}^{\circ}\). Some experimental images can be seen in Figure 6.  
Figure 5: Structure of classification network. Figure 6: Image of experimental dataset. (**a**) Simulated sphere object. (**b**) Simulated cylinder object. (**c**) Simulated truncated cone object. (**d**) Real sphere object. (**e**) Real cylinder object. (**f**) Real truncated cone object. Experiment1: Performance of Single-Channel Classification Network with Different Feature Extraction Module  
The whole network model proposed is a parallel fusion network, including two input layers, two parallel feature extraction networks, a fusion layer, and a classification layer. The two channels input different images respectively. However, the existing classification network is usually a single-channel network with only one input. Therefore, to objectively compare them with other models, we use the single-channel classification network composed of one input layer, one feature extraction network, and a classification layer compared with other models. Except that the feature extraction module is different (VGG16, Resnet50 and feature extraction network in this paper), the other structures are the same. The experimental data are highlight images segmented from simulated data of the object with three shapes. It is worth noting that the classification layer of VGG16 and Resnet itself is the same as that used in this paper, so the comparison of the feature extraction network is equivalent to the comparison of the whole single-channel model. In this experiment, 80% of the images are randomly selected for training, and the remaining are used as a test set to verify network performance. The cost function of the network is classified as cross entropy to calculate the distance between the predicted value and the real label. Optimizer RMSProp is used to optimize the parameters of the whole network. During network training, the dropout rate is set to 0.5, the batch size is 16, the learning rate is 0.0001, and the number of iterations is 100. After feature extraction is completed, classification results are obtained by using the full-connection classification layer. The object classification performance on the single-channel classification network with different feature extraction modules is shown in Table 4.  
We use the network training time (Train Time), test time (Test Time), the amount of calculation (Flops), the number of parameters (Paras), and classification accuracy (Accuracy) to measure the classification performance of the network. The train time and test time of the network refers to the time that the network classifies each image in the training and testing process. The amount of computation and the number of parameters are two important indicators to measure the complexity of the model. The amount of computation corresponds to the time complexity of the model, that is, the length of the network execution time. The number of parameters corresponds to the space complexity of the model, that is, the amount of computer memory that needs to be occupied. Accuracy is the degree to which the model classifies objects correctly. Results in Table 4 show that VGG16, as a feature extraction network, has the best classification accuracy of 90.7%, but its time cost is also high when the classification time is 6 ms during training and the test time is up to 3 ms per image. Moreover, its amount of calculation and the number of parameters are the largest of these models.