Non-invasive Growth Monitoring of Small Freshwater Fish in Home Aquariums via Stereo Vision

2026-03-06

AI Summary

This study addresses the challenge of non-invasive, precise growth monitoring of small freshwater fish in home aquariums, where refraction-induced distortions and minimal size hinder accurate measurement. The authors propose a refraction-aware stereo vision method that integrates YOLOv11-Pose for fish detection and keypoint prediction with epipolar constraints corrected for air–glass–water interface refraction to enable robust feature matching. A learning-driven keypoint quality scoring mechanism is introduced to filter out unreliable detections, followed by refraction-aware 3D triangulation to reconstruct fish keypoints and estimate body length. This work presents the first integration of refraction-aware stereo vision and pose estimation in an aquarium setting, validated on a newly curated stereo dataset of Sulawesi medaka. Results demonstrate that the quality scoring significantly enhances length estimation accuracy and that the system can be readily deployed in typical home aquarium environments.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• fish growth monitoring
• refractive distortion
• aquarium
• non-invasive measurement
• small freshwater fish

Innovation

• refraction-aware stereo vision
• YOLOv11-Pose
• 3D triangulation
• non-invasive monitoring
• aquarium fish growth