## 1 Introduction  
Fish phenotypic measurement involves the quantitative description of fish body morphology and structure. It typically includes measurements of characteristics such as body length, body width, and fat content of the fish. These measurement results can provide valuable information for fish classification, population dynamics studies, investigation of environmental adaptability, and management of fish genetic resources. The tilapia, as an important economic fish, is widely utilized in aquaculture due to its fast growth, strong adaptability, and excellent meat quality [1]. Accurate acquisition of fish body information is essential for aquaculture management and product processing. Precise identification and segmentation of fish parts contribute to monitoring the health status, growth and development, disease detection, and achieving refined aquaculture management. Currently, most methods rely on manual or semi-mechanized approaches, which are more subjective, less efficient, and less accurate, and prone to physiological damage to the fish [2]. Accurate measurement and analysis of tilapia weight, length, and abdominal fat weight can be achieved through the segmentation of specific body parts, providing effective guidance for the assessment of tilapia genetic resources [3]. However, tilapia possesses unique morphological characteristics, such as a well-developed black dorsal fin, regular black markings on the dorsal fin and tail, blunt and round tail fin, and multiple vertical black stripes along the body axis. The special morphological features and the complex underwater background interference make the identification extremely difficult. Traditional segmentation methods often fail to meet the accuracy and efficiency and still have limitations in dealing with complex backgrounds, multiple fish occlusions, and edge blurring. With the development of image processing and deep learning, semantic segmentation techniques have demonstrated significant advantages and potential in fish phenotypic measurement. Many scholars have explored the possibility of applying deep learning methods to fish phenotypic measurement and have achieved notable results. For instance, [PERSON] [4] proposed a method based on Keypoints R-CNN for the segmentation and measurement of several freshwater fish species. They used deep learning networks to extract features from freshwater fish images and employed an underwater stereo vision system for automatic species recognition and length measurement. [PERSON] [5] developed an automatic trout fin-cutting system using image processing algorithms, which can accurately locate the trout fin position and determine the cutting point location. By employing the Mask-RCNN network, [PERSON] [6] successfully accomplished fish detection and segmentation, and the model had an accuracy of 88% and a recall of 84% on the validation set. Their model used the GrabCut interactive segmentation algorithm to refine the segmentation samples at the boundaries, which greatly improved the segmentation accuracy with an MIoU of 81%. However, their method has difficulties in dealing with the problem of similar colors in the dorsal fin, tail, and abdominal regions of tilapia. In particular, when the background closely resembles the fish's natural farming environment due to lighting conditions, these techniques tend to produce errors in proximity to the fin boundary and tail. Consequently, this leads to a notable decline in the accuracy of segmentation rates. To address these issues, [PERSON] [7] used Mask-RCNN to segment fish phenotypic features in pure and complex backgrounds, respectively, and the experimental results showed that the segmentation accuracy of fish morphology in complex backgrounds was much lower than that in pure backgrounds. [PERSON] [8] used Mask-RCNN to test segmentation on a self-built dataset and achieved 84.5% IoU for single fish and 82.4% IoU for overlapping fish.