## 4 Results and Discussion
### Comparative Experimental Results  
1. Performance on UIIS: We present the results of our method on the UIIS underwater image instance segmentation dataset and compare them with other popular instance segmentation methods. As shown in Table 1, our proposed method achieves a new state-of-the-art on UIIS with an mAP score of 31.7, surpassing USIS-SAM by 2.3 points. In terms of AP50 and AP75, our method exceeds USIS-SAM by 4.5 points and 2.8 points, respectively. This indicates that our approach provides better underwater instance segmentation with higher localization accuracy. Additionally, the AASNet model has a parameter count of 27.84 M, demonstrating exceptional performance in real-time underwater instance segmentation tasks. We conduct tests on an NVIDIA P40 GPU with a batch size of 12, where the model achieves an inference time of only 28.9 ms when processing 640 \(\times\) 640 resolution images. In comparison, the WaterMask [8] model has a parameter count of 66.55 M, with an inference time of 180.5 ms under the same configuration. Clearly, AASNet offers significant advantages in computational efficiency. Figure 5 presents a visual comparison of our method with existing techniques on the UIIS dataset. It can be observed that our method excels in segmenting the overall shape of prominent instances, even in challenging regions. For example, in the second column of the figure, our method accurately segments the gaps between the tentacles of a jellyfish. Additionally, our model performs exceptionally well in predicting boundaries and details, as shown in the third and fifth columns of the figure, where the boundaries of the diver and the plankton net predicted by our model are much closer to the ground truth masks. As shown in Figure 6, the training and validation loss curves on the UIIS dataset reveal the model's performance during training. The training loss steadily decreases throughout the epochs, indicating that the model effectively learns and minimizes error on the training data. Meanwhile, the validation loss remains relatively stable, with only slight fluctuations, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Backbone** & **mAP** & **AP50** & **AP75** \\ \hline Mask RCNN [5] & ResNet-101 & 23.4 & 40.9 & 25.3 \\ Mask Scoring R-CNN [35] & ResNet-101 & 24.6 & 41.9 & 26.5 \\ Cascade Mask R-CNN [36] & ResNet-101 & 25.5 & 42.8 & 27.8 \\ BMask R-CNN [12] & ResNet-101 & 22.1 & 36.2 & 24.4 \\ Point Rend [11] & ResNet-101 & 25.9 & 43.4 & 27.6 \\ SOLOv2 [37] & ResNet-101 & 24.5 & 40.9 & 25.1 \\ QueryInst [38] & ResNet-101 & 26.0 & 42.8 & 27.3 \\ Mask2 Former [39] & ResNet-101 & 25.7 & 38.0 & 27.7 \\ RDPNet [40] & ResNet-101 & 20.6 & 38.7 & 19.4 \\ Mask Transfiner [41] & ResNet-101 & 24.6 & 42.1 & 26.0 \\ WaterMask [8] & ResNet-101 & 27.2 & 43.7 & 29.3 \\ USIS-SAM [10] & ViT-H & 29.4 & 45.0 & 32.3 \\
**AASNet** & **GELAN+LCA** & **31.7** & **49.5** & **35.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of instance segmentation methods on the UIIS Dataset. Quantitative results demonstrate that our AASNet model achieves the best segmentation performance. The bold font indicates the best effect. Figure 5: The visualization results of fish instance segmentation on the UIIS dataset. First row shows the original images, and each subsequent row displays the prediction results of different models. suggesting that the model is able to generalize well to unseen data without significant overfitting. Regarding the model's instance segmentation performance, the steady decline in training loss and stable validation loss reflect a robust ability to segment instances accurately. The model shows consistent improvements in both the training and validation phases, which indicates strong performance in segmenting objects within the UIIS dataset. The analysis of the confusion matrix in Figure 7 shows that the model performs excellently in handling the \"diver\" and \"fish\" categories, with high accuracy and minimal confusion.