## 4 Results and Discussion
### Ablation Results and Analysis  
To verify the effectiveness of different components of the proposed method, we conduct several ablation experiments on the UIIS dataset to demonstrate performance. 1. Overall Ablation Study: To analyze the importance of each proposed component, we report the overall ablation study in Table 3. We gradually add the LCA module and Dynamic Adaptive Focal Loss to the YOLOv9 baseline. The results show that the LCA module improves the mAP by 2.4, demonstrating that the LCA module effectively enhances the model's ability to handle lighting variations and color differences in complex underwater environments, optimizing the consistency of semantic information and thereby improving instance segmentation accuracy. Additionally, Dynamic Adaptive Focal Loss further increases the mAP to 31.7, with simultaneous improvements in the other two metrics, proving that this loss function performs better in classification, thus enhancing overall segmentation accuracy. Figure 11: The loss curves on the USIS10K dataset. Figure 12: The confusion matrix on the USIS10K dataset. 2. Ablation study on Linear Correlation Attention Module: We conduct ablation experiments to evaluate the placement of the LCA module at different levels of the backbone, testing it at None, P1, P2, P3, P4, and P5. As shown in Table 4, placing the module at the P4 layer yields the best results, with mAP increasing by 2.4 to 30.9 and AP50 and AP75 reaching 48.4 and 34.3, respectively. This is because the P4 layer achieves an ideal balance between spatial resolution and semantic information in the feature map. In shallower layers (such as P1 and P2), although the spatial resolution is high, most captured features are low-level, making it difficult to support precise segmentation in complex underwater scenes. In deeper layers (such as P5), while higher-level abstract features are captured, the significant reduction in spatial resolution results in insufficient precision in local feature extraction. The P4 layer occupies a critical intermediate position, effectively capturing advanced levels of abstract features while maintaining sufficient spatial resolution. Therefore, adding the LCA module at the P4 layer enhances the model's ability to capture semantic features in complex underwater environments. It preserves essential detail and improves segmentation performance, leading to better results when addressing lighting variations, color differences, and complex backgrounds. 3. Ablation study on Dynamic Adaptive Focal Loss: To analyze the effectiveness of Dynamic Adaptive Focal Loss (DAFL), we present the ablation study on the loss function in Table 5. The results show that introducing label smoothing into Focal Loss improves mAP by 0.3. This suggests that label smoothing enhances the model's generalization ability, especially for hard-to-classify samples, by reducing overfitting. Additionally, incorporating a dynamic calculation strategy based on the global mean into Focal Loss improves mAP by an additional 0.5. This demonstrates that the method automatically adjusts the model's focus on hard and easy samples, depending on the class distribution within the current training batch, thereby improving classification accuracy. When both methods are applied together, the model achieves optimal performance. Overall, Dynamic Adaptive Focal Loss significantly boosts the model's classification ability and robustness, particularly in handling highly imbalanced instance segmentation tasks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Position** & **mAP** & **AP50** & **AP75** \\ \hline None & 28.5 & 45.9 & 31.2 \\ P1 & 29.5 & 48.2 & 32.2 \\ P2 & 29.6 & 46.1 & 32.5 \\ P3 & 30.6 & 47.7 & 33.8 \\ P4 & **30.9** & **48.4** & **34.3** \\ P5 & 30.5 & 47.6 & 33.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Position comparison results.