## 4 Results and Discussion
### Comparative Experimental Results
However, for the \"aquatic plants\" and \"robots\" categories, the model exhibits some misclassification, particularly with confusion between the \"background\" class and other targets. Overall, the model effectively distinguishes most categories, but further optimization is needed, especially for categories affected by background interference in complex underwater environments. 2. Performance on USIS10K: Table 2 shows the performance comparison with state-of-the-art methods. The results of the histogram visualization of the average accuracy of each advanced method are shown in Figure 8. The experimental results demonstrate that our model performs better when the data volume is expanded. Our method achieves an mAP that is 4.6 points higher than USIS-SAM under multi-label annotations and 6.2 points higher under single-label annotations. In both annotation settings,  
Figure 6: The loss curves on the UIIS dataset. Figure 7: The confusion matrix on the UIIS dataset. AP50 and AP75 also reach optimal values, further proving the effectiveness of our approach. The visualization results on the USIS10K dataset, shown in Figure 9, demonstrate the model's outstanding performance in handling complex underwater scenes. Despite the presence of background complexity, color deviation, and the effects of light scattering and absorption in underwater images, the model accurately identifies and segments multiple instances, achieving clear separation between foreground and background. Notably, even when the ground truth labels do not fully annotate all instances, the model successfully predicts unannotated fish. This is attributed to the Linear Correlation Attention (LCA) mechanism, which effectively captures linear correlations between features, enabling consistent handling of color variations caused by lighting changes and ensuring semantic consistency. The LCA module enhances the model's understanding of target objects in complex environments, allowing it to accurately identify the contours and details of objects even under significant lighting variations, thereby significantly improving segmentation performance. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**Class-Agnostic**} & \multicolumn{3}{c}{**Multi-Class**} \\ \cline{3-8}  & & **mAP** & **AP50** & **AP75** & **mAP** & **AP50** & **AP75** \\ \hline SANet [42] & ResNet-50 & 32.8 & 64.1 & 27.3 & 23.9 & 43.5 & 24.4 \\ RDFNet [40] & ResNet-50 & 53.8 & 77.8 & 61.9 & 37.9 & 55.3 & 42.7 \\ RDFNet [40] & ResNet-101 & 54.7 & 78.3 & 63.0 & 39.3 & 55.9 & 45.4 \\ QQTR [43] & ResNet-50 & 56.6 & 79.3 & 62.6 & 19.7 & 30.6 & 21.9 \\ URank+RDPNet [40] & ResNet-101 & 52.0 & 77.0 & 62.0 & 35.9 & 52.5 & 41.4 \\ URank+QOTR [43] & ResNet-50 & 49.3 & 74.3 & 56.2 & 32.1 & 44.1 & 23.3 \\ WaterMask [8] & ResNet-50 & 58.3 & 80.2 & 66.5 & 37.7 & 54.0 & 42.5 \\ WaterMask [8] & ResNet-101 & 59.0 & 80.6 & 67.2 & 38.7 & 54.9 & 43.2 \\ SAM+BBox [44] & ViT-H & 45.9 & 65.9 & 52.1 & 26.4 & 38.9 & 29.0 \\ SAM+Mask [44] & ViT-H & 55.1 & 80.2 & 60.9 & 38.5 & 55.4 & 44.8 \\ RSPrompter [45] & ViT-H & 58.2 & 79.9 & 65.9 & 38.0 & 55.0 & 44.6 \\ URank+RPSrompter [45] & ViT-H & 50.6 & 74.4 & 56.6 & 38.5 & 55.0 & 43.3 \\ USIS-SAM [10] & ViT-H & 59.7 & 81.6 & 67.7 & 43.1 & 59.0 & 48.5 \\ \hline
**AASNet** & **GELAN+LCA** & **65.9** & **86.0** & **73.1** & **47.4** & **62.1** & **52.2** \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of instance segmentation methods on the USIS10K Dataset. Bold values indicate the best performance in each column. The bold font indicates the best effect. Figure 8: Result visualization plots of average accuracy. Figure 10 presents a visual comparison of our method with existing techniques on the USIS10K dataset. The results indicate that Dynamic Adaptive Focal Loss (DAFL) significantly enhances the ability to handle class-imbalanced data. Unlike some advanced methods that incorrectly classify divers and bubbles, our approach accurately classifies and effectively segments instances.