## 3 Experiment and Analysis
### Comparative Analysis of Splitting Performance  
In Tables 1 and 2, the performance of the proposed models is compared with several existing semantic segmentation models. Overall, these networks exhibit good recognition of the background but show relatively poor recognition results for fish fins and fish heads. The improved Deeplabv3+ network, due to the fusion of two attention mechanisms, shows significant improvements in IoU values for heads, fins, tails, and the trunk compared to the original network, with increases of 9.78%, 2.27%, 6.58%, and 6.07%, respectively. The improved Deeplabv3+ network demonstrates an increase of 3.23% in mIoU, 1.96% in mPA, and 4.74% in mRecall on the tilapia part segmentation dataset compared to the original network. Compared to other models, the improved model is able to handle boundaries and small target regions better due to the fusion of the two attentions and the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**IoU (\%)**} \\ \cline{2-6}  & **Background** & **Head** & **Fin** & **Trunk** & **Tail** \\ \hline PSPNet & 98.14 & 68.89 & 49.26 & 79.19 & 74.45 \\ U-Net & 98.90 & 80.67 & 84.70 & 65.63 & 83.65 \\ HRNet & 98.98 & 81.04 & 65.27 & 84.12 & 82.77 \\ LR-ASPP & 98.82 & 84.00 & 69.37 & 86.39 & 83.39 \\ Deeplabv3+ & 99.18 & 79.05 & 75.70 & 83.59 & 83.48 \\ Our method & 99.09 & 88.83 & 77.97 & 89.66 & 90.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: IoU comparison results of semantic segmentation network on tilapia part dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**mIoU (\%)**} & \multicolumn{2}{c}{**mPA (\%)**} & \multicolumn{2}{c}{**mRecall (\%)**} \\ \hline PSPNet & 73.99 & 83.88 & 84.60 \\ U-Net & 82.71 & 90.31 & 89.68 \\ HRNet & 82.44 & 89.83 & 89.34 \\ LR-ASPP & 84.39 & 91.23 & 91.22 \\ Deeplabv3+ & 88.46 & 93.98 & 89.67 \\ Our method & 91.69 & 95.94 & 94.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results between semantic segmentation networks. Figure 9: Comparison of training loss curves of network models. inclusion of Focal loss and Dice loss. These findings also show that the improved network is more advanced and superior. In Table 3, we conducted an extensive investigation into the performance of diverse backbone networks in tilapia anatomical segmentation. Furthermore, a novel enhancement approach was introduced. ResNet-50, MobileNetv3, Swin Transformer, Xception, and our approach (Xception + CBAM + SENet) were selected for the experiments. This selection aimed to comprehensively understand their performance contrast for this specific task. The experimental findings elucidate noticeable performance disparities among distinct backbone networks. Remarkably, the model founded on Xception as the backbone network excelled significantly in mIoU and mPA values compared to ResNet-50, MobileNetv3, and Swin Transformer, confirming Xception's exceptional proficiency in tilapia anatomical segmentation. Additionally, we enhanced the segmentation capability of the Deeplabv3+ model with Xception as its backbone network by incorporating two attention mechanisms, CBAM and SENet. In comparison, our approach achieved a substantial increase of 3.23% and 1.96% in mIoU and mPA values, respectively, compared to Xception. This significant improvement is attributed to the introduction of two attention mechanisms, CBAM and SENet, which allow the network to focus on crucial features essential for segmentation outcomes. Additionally, these mechanisms efficiently capture multi-scale features within images, thereby enhancing the model's ability to effectively address intricate details in tilapia anatomical segmentation tasks. In Figure 10, it can be observed that the Deeplabv3+ network based on the fusion of CBAM and SENet attention mechanisms successfully predicts the tilapia fish and achieves good prediction results for various body parts.