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

TL;DR

This work proposes a non-invasive refraction-aware stereo vision method to estimate fish length in aquariums that uses a YOLOv11-Pose network to detect fish and predict anatomical keypoints on the fish in each stereo image.

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.

Figures (4)

• Figure 1: Example from our dataset. This image shows the upper left quadrant of a frame from our dataset. The fish only cover a small area of the image and partly hide between plants.
• Figure 2: Example of annotated Sulawesi ricefish from our dataset. The annotation includes a bounding box, five keypoints, and a quality class. The fish at the bottom is labeled high quality (Q3) because its keypoints are clearly visible, while the fish at the top (green bounding box) is labeled medium quality (Q2) because its keypoints are hard to recognize due to motion blur. The fish with the pink bounding box is labeled as low quality (Q1) because its keypoints are barely visible due to its low contrast.
• Figure 3: Annotated examples frames of the test-only scenes. In one scene (right), the back of the aquarium was covered with white paper to simulate an aquarium close to a wall.
• Figure 4: Our approach applies a YOLOv11-Pose separately on the two images of the stereo pair. The detected fish instances are then matched and different components are applied to improve keypoint positions and remove poorly estimated predictions. The individual improvement and quality assessment components are introduces in Section \ref{['subsec:Piepeline']}. In brackets are the components' abbreviations used in the results section.