用3D视觉与深度学习，自动监测小鱼运动状态，提前预警健康问题。
Automated 3D Kinematic Monitoring for Circadian Activity and Anomaly Detection in Juvenile Fish
- 结合双目立体视觉与深度学习，实时重建小鱼三维运动轨迹。
- 首次实现自由游动幼鱼的三维速度与加速度精确测量。
- 适合水产养殖、动物行为研究者用于健康监测与活力评估。
精准水产养殖面临表型分析瓶颈，传统方法无法量化高分辨率三维生理活动。为此，我们提出一种高通量3D行为表型框架，融合深度学习目标检测与双目立体视觉，实现实时监测高密度环境中幼罗非鱼的三维运动。系统自动完成非接触式体长估计，并基于绝对空间坐标重建3D游泳轨迹。通过消除二维视角畸变，该方法精确量化3D速度与加速度，首次实现对自由游动幼鱼真实游泳速度的估算。结果表明，该框架成功建立昼夜活动基线，可作为生理应激的早期预警系统，并提供鱼类活力的客观度量指标。
原文摘要 · Abstract (English)
Precision aquaculture faces a "phenotyping bottleneck" in tracking high-resolution behavioral traits, as conventional methods cannot quantify instantaneous three-dimensional (3D) physical exertion. To address this, we present a high-throughput 3D behavioral phenotyping framework integrating deep learning object detection with binocular stereo vision for real-time monitoring of juvenile tilapia in high-density environments. The system automates non-contact body length estimation and reconstructs 3D swimming trajectories from absolute spatial coordinates. By eliminating 2D perspective distortions, this approach precisely quantifies 3D velocity and acceleration, marking the first estimation of true physical swimming speeds in free-roaming juveniles. Results show the framework successfully establishes circadian locomotor baselines, serving as an early warning system for physiological stress and providing an objective metric for fish vitality.
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