Autonomous underwater stereo vision system for non-invasive fish length estimation in marine environments

Marine ecosystems are undergoing rapid change due to climate and human pressures, increasing the need for monitoring tools. Fish length data are particularly valuable for assessing population and ecosystem status, yet conventional measurement methods are often time-consuming and invasive. This study introduces a practical, non-invasive pipeline for automatic fish length estimation based on an underwater Stereo-Vision System (SVS) that provides synchronized image pairs for 3D reconstruction. Fish length is estimated by combining 2D segmentation masks with reconstructed 3D scene information, with a multi-object tracking module that enables track-based aggregation of frame-wise estimates to improve robustness. A YOLOv11-based segmentation model together with a BoT-SORT tracker is employed to detect, segment and track target species. The proposed method is evaluated on two datasets of increasing complexity, including controlled multi-species scenarios and live underwater footage in controlled conditions. It achieves a Mean Absolute Error (MAE) of 1.30 cm, a standard deviation of 1.68 cm, a Mean Absolute Percentage Error (MAPE) of 4.53%, and an overall success rate of 49.7%, while operating under a conservative filtering strategy based on geometric and track-level consistency constraints. The pipeline is further integrated into an autonomous SVS, enabling real-time on-board operation in marine environments. Results demonstrate that accurate and robust fish length estimation can be achieved under challenging underwater conditions, supporting the deployment of scalable, non-invasive monitoring solutions for ocean observation.