Aquaculture increasingly demands continuous, low-disturbance, and automated monitoring of fish growth status to reduce feeding costs, assess health and yield, and support modern intelligent farming. However, in real production settings, growth-state assessment still largely relies on prolonged manual observation, which is labor-intensive, subjective, and difficult to standardize across operators and time, thereby limiting the feasibility of continuous, automated acquisition of fish growth morphometric indicators. Pose estimation based on computer vision, which can locate the positions of keypoints on the fish body, is a crucial method for obtaining morphometric indicators(e.g., total length, s