## 1 Introduction
To extract fish contours, [PERSON] [9] improved mAP by 2.6% compared to the original model by improving Decoupled-SOLO, but they still need to solve the problem of fish occlusion. In addition, [PERSON] [10] compared and tested FCN [11] with the Segnet [12] network on a self-built semantic part segmentation dataset of striped sharks, and the experimental findings demonstrated that Segnet was more accurate on the self-built dataset and the segmentation was smoother in each part. To enhance the accuracy of segmentation within intricate underwater environments, [PERSON] [13] used Deeplabv3+ as the base model, introduced an unsupervised color correction module (UCM) in the encoder part to improve the image quality, two upsamplings were added to the decoder part, and verified the improved method's segmentation accuracy improvement of 3% on a homemade dataset. However, the above method degrades when dealing with different types of objects of similar shapes or small objects for semantic segmentation, in addition to the segmentation effect when the gap between the target and the background is small. [PERSON] [14] addressed the problems of inaccurate image target edge segmentation and slow image fitting by the Deeplabv3+ model and mirrored the feature cross-notice module (FCA). The improved model improves MIoU and MPA on the Pascal VOC2012 dataset by 1.96% and 2.84%, respectively. Since FCA consists of two branches and a feature cross-attention module, it will occupy too much computational space and is very demanding on the equipment. Although many domestic and foreign researchers have performed a lot of research on fish segmentation and detection, no method for tilapia part segmentation has been found after review. In addition, based on the application of the Deeplabv3+ semantic segmentation model, many scholars have achieved good results in different fields. [PERSON] [15] realized the accurate segmentation of lithich branches by using the Deeplabv3+ model combined with the Xception_65 feature extraction network and obtained the relevant information of the branches through semantic segmentation, which provided powerful picking robots to find the fruit branch. For the research area of automatic interpretation of remote sensing images, [PERSON] [16] used the combination of the Deeplabv3+ network with different models, such as ResNet-50, and compared the model's performance on different datasets, in which the IoU value of Deeplabv3+ and ResNet-50 model in the Inria dataset was at least 3% higher than that of the previous studies. These studies provide useful guidance for the task of tilapia part segmentation. Based on the above, we selected the Deeplabv3+ network as the baseline framework. In choosing Deeplabv3+ as the basic framework, we consider its excellent performance in semantic segmentation. Deeplabv3+ is able to effectively capture both global and local features in an image through the features of dilated convolution and global contextual information fusion, and it is also robust in dealing with scale changes and object boundaries. For the task of tilapia part segmentation, Deeplabv3+'s multi-scale information fusion allows the network to perceive the changes in different scales of the morphological features of tilapia, such as dorsal fins and tails, which is expected to capture the features better and improve the accuracy of segmentation. Furthermore, a self-constructed tilapia body part segmentation dataset was used as our experimental dataset. Considering the limitations of the Deeplabv3+ network in accurate object edge segmentation and ambiguous segmentation of small objects, this study proposes a fusion of CBAM (Convolutional Block Attention Module) [17] and SENet [18] with the Deeplabv3+ model. The CBAM attention mechanism is introduced after the feature extraction stage, allowing the model to assign different weights to different spatial and channel aspects of the input image, and SENet can be used to enhance the informative features and suppress the unimportant ones.