# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 4 Experimental Results
### Comparison of Model  
During the training phase, the loss function curves for YOLOv9s and its different modified iterations were documented in Figure 5. All four models displayed a swift decrease in loss function values at the beginning of training, with limited overall variability. By the 20 th epoch, the average loss value for each model approached approximately 0.06, and the ultimate losses settled at around 0.043, suggesting a satisfactory convergence among all models. From the training results, the YOLOv9s-UI model achieved a maximum accuracy of 0.7568 (i.e., extracted from the peak value of the last decile of the data due to fluctuations in accuracy). The highest recall obtained was 0.8191. The model's maximum mAP was 0.7812 at a confidence threshold of 0.5, while the highest mAP within confidence thresholds of 0.5 to 0.95 was 0.4874. In Table 2, AL-YOLOv9s demonstrates a clear superiority in accuracy and outperforms models of similar scale in various metrics. In Figure 6 and Table 3, the improved YOLOv9s-UI model significantly enhances its practicality by increasing detection accuracy and recall rates in the fields of marine research and underwater surveillance,. The model achieves high precision (0.7568) and maximum recall (0.8191), enabling more accurate identification and classification of marine life in complex underwater environments, which is crucial for the monitoring and protection of biodiversity. Additionally, the model's robust performance across different confidence thresholds indicates its adaptability to varied monitoring needs, optimizing detection precision and efficiency in applications such as ship detection or subsea pipeline monitoring. With the integration of the D-Mixer module and the local channel and spatial attention mechanisms, the YOLOv9s-UI model outperforms similar models in precision, recall, and mean Average Precision (mAP) metrics, demonstrating its capability to handle complex tasks in marine environments, such as tracking moving targets or conducting visual analysis under harsh weather conditions. These technological advancements not only enhance the model's performance but also increase its broad applicability and reliability in practical applications like marine research and underwater surveillance. Figure 5: Training loss. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Baseline** & **D-Mixer** & **Local CA\&SA** & **Precision (\%)** & **Recall (\%)** & _[EMAIL_ADDRESS] (\%)_ & _[EMAIL_ADDRESS] (\%)_ \\ \hline YOLOv9 & \(\times\) & \(\times\) & - & - & - & - \\ \hline \multirow{4}{*}{YOLOv9s} & \(\times\) & \(\times\) & 72.62 & 78.51 & 77.79 & 48.01 \\ \cline{2-6}  & \(\surd\) & \(\times\) & 74.92 & 81.91 & 77.65 & 47.66 \\ \cline{2-6}  & \(\times\) & \(\surd\) & 69.47 & 75.11 & 77.10 & 47.37 \\ \cline{2-6}  & \(\surd\) & \(\surd\) & 75.68 & 80.20 & 78.12 & 48.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of evaluation indexes of YOLOV9 and YOLOV9s models and their improved modules. Figure 6: Comparison of train curves. Compared to the original YOLOv9s model, the YOLOv9s-UI model shows enhanced performance across various categories, in Table 4. Specifically, for sea cucumbers and scallops, YOLOv9s-UI exhibits the capability to detect these species effectively, a task that \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **YOLOv9s** & **YOLOv9s-UI** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example of detection results. posed challenges for the original network. This indicates improved feature extraction and increased performance within the improved network structure. For sea urchins, there is a significant improvement in detection accuracy, showcasing substantial progress in identifying sea urchin characteristics. The model also displays increased accuracy in detecting starfish, highlighting improved precision in both recognition and localization.