用双目视觉精准测量鱼缸中小鱼长度，克服折射干扰。
Non-invasive Growth Monitoring of Small Freshwater Fish in Home Aquariums via Stereo Vision
- 结合姿态检测与折射校正的双目立体视觉方法
- 在真实鱼缸环境下实现毫米级长度测量，误差显著降低
- 适合家庭养鱼者或科研人员做无创生长监测
鱼的生长行为监测可反映其健康状况，但小鱼在鱼缸中受空气-玻璃-水界面强烈折射影响，难以准确测量。本文提出一种非侵入式、考虑折射的双目视觉方法，利用YOLOv11-Pose网络在左右图像中检测鱼体并预测关键点。通过引入考虑折射的对极约束进行鲁棒匹配，并基于学习的质量评分剔除低质量检测结果。随后采用折射校正的3D三角测量恢复关键点三维坐标，进而计算鱼体长度。我们在模拟鱼缸环境下的濒危苏拉威西米鱼新采集的双目数据集上验证了该方法，结果表明剔除低质量检测是获得精确长度估计的关键。系统简单实用，可直接应用于家庭鱼缸的无创生长监测。
原文摘要 · Abstract (English)
Monitoring fish growth behavior provides relevant information about fish health in aquaculture and home aquariums. Yet, monitoring fish sizes poses different challenges, as fish are small and subject to strong refractive distortions in aquarium environments. Image-based measurement offers a practical, non-invasive alternative that allows frequent monitoring without disturbing the fish. In this paper, we propose a non-invasive refraction-aware stereo vision method to estimate fish length in aquariums. Our approach uses a YOLOv11-Pose network to detect fish and predict anatomical keypoints on the fish in each stereo image. A refraction-aware epipolar constraint accounting for the air-glass-water interfaces enables robust matching, and unreliable detections are removed using a learned quality score. A subsequent refraction-aware 3D triangulation recovers 3D keypoints, from which fish length is measured. We validate our approach on a new stereo dataset of endangered Sulawesi ricefish captured under aquarium-like conditions and demonstrate that filtering low-quality detections is essential for accurate length estimation. The proposed system offers a simple and practical solution for non-invasive growth monitoring and can be easily applied in home aquariums.
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