# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
## 3 Experiments
### Performance of Target Detection Model  
Given that the purpose of this study is to enhance images of underwater targets acquired from SSS in order to enhance the effectiveness of deep learning-based target detection models, this section conducts performance evaluation experiments utilizing a deep learning approach for the detection of targets. Currently, numerous target detection models are available. Considering the high speed, high precision, and lightweight features of the YOLOv5, this study employs the YOLOv5 model.  
Using images of ships and aircrafts, three experimental sets with different dataset sizes were designed to train the YOLOv5 model. The datasets comprise a collection of authentic images, a compilation of augmented images, and a blend of both authentic and augmented images. The dataset distribution is shown in Table 2. The augmented ships and aircraft images were selected after filtering out low-quality images. To assess the effectiveness of the target detection model, 200 authentic SSS images were chosen.  
After training the model, 200 authentic SSS images were employed to evaluate the model. The evaluation criteria utilized comprise precision, recall, and mean average precision. The results of the detection are detailed in Table 3.  
From Table 3, it can be observed that, when compared to YOLOv5-1 and YOLOv5-2, the target detection model trained using the image augmentation technique proposed in this study achieves superior precision, recall, and mean average precision for aircraft and ship targets. This indicates that augmented images play a crucial role in improving the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Set** & **Authentic Images** & **Augmented Images** \\ \hline
1 & 50 & - \\
2 & - & 450 \\
3 & 50 & 450 \\ Detection Images & 200 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of datasets and validation sets for YOLOv5 detection model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Class** & **Precision** & **Recall** & **mAP0.5** & **mAP0.5:0.95** \\ \hline \multirow{4}{*}{YOLOv5-1} & ship & 90.1\% & 93.8\% & 0.925 & 0.697 \\  & aircraft & 89.5\% & 86.4\% & 0.911 & 0.653 \\  & all & 89.8\% & 90.1\% & 0.918 & 0.675 \\ \cline{2-6}  & ship & 92\% & 93.6\% & 0.955 & 0.824 \\ \cline{2-6}  & aircraft & 98.7\% & 95\% & 0.946 & 0.743 \\ \cline{2-6}  & all & 95.3\% & 94.3\% & 0.951 & 0.784 \\ \cline{2-6}  & ship & 96.8\% & 96.1\% & 0.961 & 0.879 \\ \cline{2-6}  & aircraft & 98\% & 94.1\% & 0.948 & 0.733 \\ \cline{2-6}  & all & **97.4\%** & **95.1\%** & **0.955** & **0.806** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of diverse training sets on the detection of authentic SSS target images.  
model's detection capabilities. YOLOv5-1, which exclusively utilizes authentic images, has the lowest precision, recall, and mean average precision, highlighting that the amount of data are a key factor affecting model detection performance. After image augmentation, the dataset YOLOv5-3, which includes both real and augmented images, shows significant improvements in precision, recall, and mean average precision. This demonstrates that the performance improvement is mainly attributed to the proposed method for generating augmented SSS images, which effectively augments the underwater SSS image data.  
To further illustrate the impact of various training datasets on the identification of authentic side-scan sonar target images, some detection results using different training sets are presented in Figure 8. As shown in Figure 8, the confidence in detecting the corresponding categories is highest when using the third dataset and lowest when using the first dataset.