# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 5 Conclusions and Future Works  
In the field of underwater object detection, this study extends the YOLOv9s model by introducing the YOLOv9s-UI detection model. To augment the feature extraction capabilities, the D-Mixer module from TransXNet has been integrated. The D-Mixer module dynamically adjusts token distribution during feature extraction, thereby enhancing the model's capacity to identify targets in complex underwater settings. Furthermore, inspired by the feature fusion structure of the LocalMamba network, a network incorporating channel and spatial attention mechanisms has been devised. The channel attention module and spatial attention module amplify the significance of different feature channels and spatial locations within the feature maps, respectively, facilitating feature fusion. These improvements increase the accuracy of the model in underwater object detection, while maintaining a modest model size of 9.3 M, ensuring effectiveness and ease of deployment. Experimental results illustrate that the YOLOv9s-UI model excels in underwater object detection tasks, fulfilling the requirements for high detection precision and convenient \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**D-Mixer**} & \multicolumn{2}{c}{**Local**} & \multirow{2}{*}{**Misidentification**} & \multicolumn{2}{c}{**Accuracy**} & \multirow{2}{*}{**FPS**} & \multicolumn{2}{c}{**Inference**} \\  & & & **CA\&SA** & & **(\%)** & **Time (ms)** \\ \hline YOLOv9 & \(\times\) & \(\times\) & - & - & - & - \\ \hline \multirow{5}{*}{YOLOv9s} & \(\times\) & \(\times\) & 126 & 73.19 & 32.97 & 14,254 \\ \cline{2-7}  & \(\surd\) & \(\times\) & 123 & 73.83 & 32.56 & 14,437 \\ \cline{1-1} \cline{2-7}  & \(\times\) & \(\surd\) & 140 & 70.21 & 32.33 & 14,537 \\ \cline{1-1} \cline{2-7}  & \(\surd\) & \(\surd\) & 112 & 76.17 & 32.25 & 14,573 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model evaluation metrics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**D-Mixer**} & \multicolumn{2}{c}{**Local**} & \multirow{2}{*}{**Layers**} & \multicolumn{2}{c}{**Parameter**} & \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**FLOPs/G**} \\  & & & **CA\&SA** & & **Quantity** & **Size/MB** & \\ \hline YOLOv9 & \(\times\) & \(\times\) & 727 & 60,512,080 & 122.4 & 264.0 \\ \hline \multirow{5}{*}{YOLOv9s} & \(\times\) & \(\times\) & 727 & 4,223,998 & 9.1 & 18.4 \\ \cline{2-7}  & \(\surd\) & \(\times\) & 751 & 4,247,566 & 9.2 & 18.5 \\ \cline{1-1} \cline{2-7}  & \(\times\) & \(\surd\) & 771 & 4,298,552 & 9.3 & 18.5 \\ \cline{1-1} \cline{2-7}  & \(\surd\) & \(\surd\) & 779 & 4,308,934 & 9.3 & 18.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of model complexity. portability. Due to its high computational demands, the YOLOv9s-UI model faces limitations that restrict its use in resource-constrained environments. Currently, it has only been validated on the URPC2019 dataset. Additionally, the complexity of its advanced modules makes deployment in various applications challenging, thus further optimization is needed to achieve broader usability. Future research will focus on refining the YOLOv9s-UI model by exploring more effective feature extraction and fusion techniques to enhance its detection capabilities. This includes integrating advanced attention mechanisms and modular designs to improve the model's performance in complex underwater environments. Furthermore, we provide a new research direction for underwater object detection, i.e., jointing multi-tasks during training and using other task loss to improve the detection task performance. We plan to expand the testing of the model across a wider range of datasets to assess its generalization capabilities and optimize its performance in various real-world scenarios.