# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 3 Experiment and Results
### Target Detection Experiment
#### 3.2.1 Target Detection Only Using the Improved Yolov8  
To expand the sample size, conventional data augmentation methods, including mirroring, flipping, rotation, scaling, and denoising, were used. In this part, data augmentation using CycleGAN was not used. The sample number of each classification is shown in Table 1.  
Figure 8: Comparison of pseudo-side-scan sonar images generated using different models. In rows (**A**,**B**), images contain aircraft targets. In rows (**C**–**E**), images contain ship targets.  
The ablation study aims to determine the necessity of specific modules by adding or removing them. To validate our revisions on the original YOLOv8 model, an ablation study was conducted by gradually adding our revisions (namely, C2f-DCNv3, GAM, and EIoU loss), and the results of different models are shown in Table 2. Based on the original YOLOv8, the accuracy of [EMAIL_ADDRESS] increased by 3%, 10.6%, and 13% after sequentially introducing C2f-DCNv3, GAM, and EIoU Loss, respectively. These results demonstrated that the proposed revision improved the detection accuracy of the YOLOv8 model. The processing time for a single image by the improved YOLOv8 model reached 36.5 ms, which was higher than the time cost (22.1 ms) of the original YOLOv8 model. Our model could still meet the real-time requirement for target detection in side-scan sonar images.  
The target detection result of the original YOLOv8 and improved YOLOv8 on the dataset (excluding pseudo-side-scan sonar image data augmentation) is shown in Figure 9. The original YOLOv8 had many detection failures and only achieved the detection accuracy [EMAIL_ADDRESS] of 0.771, whereas our improved YOLOv8 increased [EMAIL_ADDRESS] to 0.901. After improvements, the overall detection accuracy increased by 13%, and the detection accuracy of ship and plane targets increased by 8.2% and 17.6%, respectively.  
To further validate the performance of the improved model, our model was compared with other modern target detection models on the same dataset, and the results are shown in Table 3. The [EMAIL_ADDRESS] of DETR [32], which was a two-stage target detection model, was only 41.2%, whereas the [EMAIL_ADDRESS] of the original YOLOv8, YOLOv9 [33], YOLOv10 [34], and YOLOv11 [35] were 77.1% and 82.5%, respectively. The improved YOLOv8 model obtained 90.1% of [EMAIL_ADDRESS], which was 13% and 7.6% higher than those of the original YOLOv8 and YOLOv9, and 15.6% and 13.6% higher than those of the latest YOLOv10 and YOLOv11, respectively. Moreover, our model had the highest [EMAIL_ADDRESS]:0.95, P, R, and F1 scores. These results showed the advantages of our improved YOLOv8 model over the other models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Sample Number of Each Classification**} \\ \cline{2-6}  & **Plane** & **Plane** & **Ship** & **Ship** \\  & **(Pseudo Images)** & **(Pseudo Images)** & **(Pseudo Images)** \\ \hline Training & 380 & / & 610 & / \\ Validation & 190 & / & 300 & / \\ Test & 60 & / & 100 & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample number of each classification in the dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **Time Cost Per Image (ms)** \\ \hline YOLOv8 & 77.1 & 42.6 & 71.1 & 70.2 & 70.6 & 22.1 \\ YOLOv8 + & & & & & & \\ C2f-DCNv3 & 80.1 & 43.7 & 81.6 & 74.3 & 77.8 & 31 \\ YOLOv8 + GAM + & & & & & & \\ C2f-DCNv3 & 87.7 & 45.3 & 84.8 & 81.8 & 83.3 & 35.3 \\ Our improved YOLOv8 & 90.1 & 54.0 & 86.6 & 82.5 & 84.5 & 36.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiment.