# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 3 Experiment and Results
### Target Detection Experiment
#### 3.2.2 Target Detection Using CycleGAN and the Improved Yolov8  
CycleGAN was used for generated pseudo images. To analyze how different proportions of real and pseudo images influence target detection accuracy, six different datasets with different real/pseudo proportions were set up, as shown in Table 4. The training set of Dataset F only contained pseudo images. From Datasets A to D, the pseudo images in the training set were sequentially 1 to 4 times the number of real ones. For the plane targets, the classification accuracy increased with the increased number of pseudo images in Dataset A to C but decreased in Dataset D. For the ship targets, the classification accuracy slightly increased with the growing number of pseudo images in Dataset A to D. In addition, if the model were trained only on pseudo images of Dataset F and evaluated on real sonar images, the performance of target detection would degrade compared with the performances on Datasets A to D.  
Based on the above dataset, in Dataset E, the pseudo images containing plane targets were three times that of the real ones. To balance the sample sizes of plane and ship in the training set, the number of pseudo images containing ship targets was 1.8 times that of the real ones. Therefore, the YOLOv8 model achieved the highest classification accuracies (0.91 and 0.97 for plane and ship targets, respectively) on Dataset E. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline DETR & 41.2 & / & 37.8 & 46.3 & 41.6 \\ YOLOv8 & 77.1 & 42.6 & 71.1 & 70.2 & 70.6 \\ YOLOv9 & 82.5 & 42.2 & 79.1 & 73.1 & 76.0 \\ YOLOv10 & 74.5 & 38.3 & 70.4 & 64.6 & 67.4 \\ YOLOv11 & 76.5 & 36.8 & 73.8 & 68.1 & 70.8 \\ Our Improved YOLOv8 & 90.1 & 54.0 & 86.6 & 82.5 & 84.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different target detection models on the same dataset. Figure 9: Comparison of precision–recall curves of detection accuracy on sonar dataset using the original YOLOv8 (**a**) and the improved YOLOv8 (**b**). The confusion matrices of classifications using our improved YOLOv8 on Dataset A to F are shown in Table 5.  
To demonstrate the advantage of combining the CycleGAN and the improved YOLOv8 models, a bottom detection experiment using our model on Dataset E was conducted, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Augmented Side-Scan** & **Plane** & **Plane** & **Ship** \\
**Sonar Image Dataset** & & **(Pseudo** & **Ship** & **(Pseudo** \\  & & **Images**) & & **Images**) \\ \hline \multirow{3}{*}{A} & Training & 380 & 380 & 610 & 610 \\  & Validation & 190 & / & 300 & / \\  & Test & 60 & / & 100 & / \\ \hline \multirow{3}{*}{B} & Training & 380 & 760 & 610 & 1220 \\  & Validation & 190 & / & 300 & / \\  & Test & 60 & / & 100 & / \\ \hline \multirow{3}{*}{C} & Training & 380 & 1140 & 610 & 1830 \\  & Validation & 190 & / & 300 & / \\  & Test & 60 & / & 100 & / \\ \hline \multirow{3}{*}{D} & Training & 380 & 1520 & 610 & 2440 \\  & Validation & 190 & / & 300 & / \\  & Test & 60 & / & 100 & / \\ \hline \multirow{3}{*}{E} & Training & 380 & 1140 & 610 & 1100 \\  & Validation & 190 & / & 300 & / \\  & Test & 60 & / & 100 & / \\ \hline \multirow{3}{*}{F} & Training & / & 2040 & / & 2440 \\  & Validation & 190 & / & 300 & / \\ \cline{1-1}  & Test & 60 & / & 100 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sample numbers of real and pseudo images in the five different datasets.