# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
## 4 Experiment Results and Analysis
### Comparative Experiment  
For comparative analysis, this study evaluates multiple object detection networks, including Fast R-CNN, YOLOv11s, YOLOv7 tiny, YOLOv11n, YOLOv5s, and YOLOv8n, using precision (P), recall (R), [EMAIL_ADDRESS], and parameter count as evaluation metrics. The  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Group** & \multicolumn{4}{c}{**Method**} & \multicolumn{2}{c}{**Pre/\% R/\%**} & **mAP50** & **GFLOs** & **Para/(M)** & **FPS** \\ \hline  & YOLOv11n & FASFFHead & EEA & C3K2\_Sc & Focaler-IOU & & & & & \\
1 & \(\surd\) & \(\surd\) & & & & 76.2 & 75.6 & 77.2 & 6.3 & 2.6 & 36 \\
2 & \(\surd\) & \(\surd\) & & & & 76.4 & 78.4 & 77.6 & 13.3 & 4.0 & 16 \\
3 & \(\surd\) & & \(  
performance of the improved YOLOv11 network and baseline models on the test set is comprehensively summarized in Table 3 (all experimental results in Table 3 were obtained through direct training using real data). Faster-RCNN, a two-stage algorithm, has a larger parameter size and computational load than one-stage algorithms, resulting in slower detection speeds. Moreover, on a dataset dominated by small objects, its [EMAIL_ADDRESS] is 5.2% lower than that of the algorithm presented in this paper. In comparison to the traditional lightweight detection algorithms YOLOv11n and YOLOv8n, the algorithm introduced in this paper shows a slight increase in parameter size but a significant improvement in all detection metrics. When matched against the more heavyweight YOLOv11s and YOLOv5s, the algorithm has approximately one-third of its parameter size yet achieves higher accuracy, recall, and FPS. It is evident that SFE-YOLO surpasses other network models in multiple parameters while maintaining a relatively small parameter size. With an FPS of 17, it satisfies general real-time detection requirements. Therefore, the SFE-YOLO algorithm enhances the accuracy of small target detection in sonar images while meeting high frame rate requirements and effectively balances detection accuracy and speed.  
To further demonstrate the advantages of the SFE-YOLO algorithm and the pre-training strategy using ADA-StyleGAN3-generated data, we conducted comparative experiments combining these two algorithms and strategies. The experimental results are shown in Table 4.  
As shown in the table above, given the scarcity of sonar image data, the strategy of first pre-training on generated data and then conducting transfer training on real data is effective. It improves the final model's performance for both the baseline YOLOv11n and SFE-YOLO networks.  
For YOLOv11n, this strategy boosts accuracy by 1.1%, recall by 0.7%, and mAP50 by 0.2%. For SFE-YOLO, accuracy increases by 1.6%, recall by 1.1%, and mAP50 by 1.8%. These results confirm that pre-training on synthetic data and then transfer training on real data reduces model generalization error. It also decreases the model's reliance on large amounts of real data and improves the recognition accuracy of models trained on small-sample sonar data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Pre/\%** & **R/\%** & **mAP50** & **GFLOs** & **Para/(M)** \\ \hline YOLOv11n & 75.1 & 74.9 & 77.0 & 6.3 & 2.6 \\ SFE-YOLO & 90.4 & 89.2 & 87.9 & 13.0 & 3.7 \\ YOLOv11n + pre-trained model & 76.2 & 75.6 & 77.2 & 6.3 & 2.6 \\ SFE-YOLO + pre-trained model & 92.0 & 90.3 & 89.7 & 13.0 & 3.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transfer Learning Comparative Experiment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Pre/\%** & **R/\%** & **mAP50** & **Para/(M)** & **FPS** \\ \hline Fast R-CNN & 83.2 & 82.1 & 82.7 & 41.0 & 4 \\ YOLOv11s & 85.3 & 86.3 & **88.1** & 9.4 & 15 \\ YOLOv7 tiny & 81.3 & 80.5 & 80.9 & 6.1 & 22 \\ YOLOv8n & 78.2 & 77.5 & 77.8 & 3.0 & 38 \\ YOLOv5s & 84.5 & 83.7 & 84.1 & 9.1 & 16 \\ YOLOv11n & 75.1 & 74.9 & 77.0 & 2.6 & 36 \\ SFE-YOLO & 90.4 & 89.2 & 87.9 & 3.7 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metric comparison table of SFE-YOLO and other models.