Non-invasive Growth Monitoring of Small Freshwater Fish in Home Aquariums via Stereo Vision
This work provides a practical, non-invasive method for fish growth monitoring, which is valuable for aquaculture and home aquarium enthusiasts concerned with fish health, offering an incremental improvement over existing methods.
This paper addresses the challenge of non-invasively monitoring the growth of small freshwater fish in home aquariums, which is complicated by their small size and refractive distortions. They propose a refraction-aware stereo vision method that uses YOLOv11-Pose for keypoint detection and a refraction-aware epipolar constraint for robust matching and 3D triangulation, enabling accurate fish length measurement.
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.