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

TL;DR

This paper introduces a non-invasive stereo vision method utilizing deep learning and refraction-aware constraints to accurately measure fish length in aquariums, facilitating growth monitoring without disturbing the fish.

Contribution

It presents a novel refraction-aware stereo vision approach with deep learning for precise fish length estimation in aquarium environments, addressing refractive distortions.

Findings

Accurate fish length estimation demonstrated on a new dataset.

Filtering low-quality detections improves measurement accuracy.

Method is practical for home aquarium growth monitoring.

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.