## 3 Experiment and Analysis
### Comparative Analysis of Splitting Performance
Although the U-Net network achieves good results on the tilapia part segmentation dataset, it performs noticeably lower in predicting the tail compared to our proposed method. This is because the tilapia fish's tail is black, and there is a small color difference between the tail fin and the background in the aquaculture environment, making it more challenging for the network to extract features accurately. In addition, in aquaculture environments, the segmentation of the caudal and fins of tilapia is interfered with by light refraction, water flow effects, and particulate matter in the water. These factors lead to unclear segmentation of the caudal and fins as well as bring challenges to the extraction of shape and texture information. In our method, we incorporate CBAM and SENet into Deeplabv3+, which greatly improves the network's feature extraction capability and suppresses the influence of irrelevant background. Furthermore, our proposed method also improves the accuracy of fish fin segmentation, addressing the low segmentation accuracy of the Deeplabv3+ network in that area. When delving into the study of tilapia fish part segmentation, our objective is to furnish robust support for future phenotypic measurements while enhancing the segmentation performance of the Deeplabv3+ model in experiments. Recognizing the significance of precisely delineating distinct organism components, we acknowledge the consequential impact on subsequent phenotypic measurements, given its potential to supply pivotal data for subsequent morphological analyses and investigations into biological behaviors. In order to validate the enhanced segmentation performance of the modified Deeplabv3+ model in real-world settings, a range of scenarios was chosen to simulate fish within authentic environments. These scenarios encompass swimming at an angle, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mIoU (\%)** & **mPA (\%)** \\ \hline Deeplabv3++ ResNet-50 & 67.54 & 84.11 \\ Deeplabv3++ MobileNetv3 & 72.26 & 86.07 \\ Deeplabv3++ Swin Transformer & 75.71 & 84.99 \\ Deeplabv3++ Xception & 88.46 & 93.98 \\ Deeplabv3+ + Xception+ CBAM+ SENet & 91.69 & 95.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of segmentation model using different feature extraction networks. horizontal stillness, multiple fish presence, relative swimming, and fish occlusion. The outcomes are illustrated in Figure 11. The proposed method is compared with U-Net, PSPNet, HRNet [30], LR-ASPP [31], and Deeplabv3+ for evaluation. U-Net tends to lose some details in fish fin segmentation and may mistakenly identify water plants as part of the fish fins. PSPNet exhibits overly rough segmentation for small objects, resulting in incomplete boundary segmentation. It also lacks robustness in distinguishing fish body parts when dealing with multiple fish or fish occlusion. Although HRNet and LR-ASPP are able to capture multi-scale information, their global contextual information fusion is not as powerful as Deeplabv3+, and the prediction on the part dataset cannot fully take into account the effect of part morphology on segmentation. As a result, difficulties arise in the recognition of tilapia's fin parts, and it is easy to segment incompletely. Deeplabv3+ can accurately segment the tilapia fish parts from the images, but compared to our proposed method, it still loses many details and exhibits issues such as missing fish fin segmentation and misclassification at the boundaries. The orange boxes in the figure indicate the details missed and misclassified by the Deeplabv3+ model during segmentation. Compared to our proposed method, the incorporation of CBAM considers the importance of pixels in different spatial positions and channels. It can focus on key features and spatial connections in the tilapia fish body parts, resulting in more complete and accurate feature extraction for fish parts and edge details. The introduction of SENet can help the model to better distinguish features in different parts of the body.