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

Clemens Seibold; Anna Hilsmann; Peter Eisert

cs.CVMar 6, 2026

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

Authors: Clemens Seibold, Anna Hilsmann, Peter Eisert

Organizations: Humboldt University of Berlin, Berlin, Germany · Fraunhofer HHI, Berlin, Germany

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 & tables

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.

# images:	104
# annotated fish:	4,331
keypoints per fish:	5	(mouth, eye, dorsal fin
		ventral fin, caudal fin)
label per fish:		quality of visibility
		(high, medium, low)

Table 1: Summary of the Sulawesi Ricefish Stereo Dataset

Panel of Figure 3 — 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 4.2 . In brackets are the components’ abbreviations used in the results section.

ground-truth	predicted quality
quality	low	medium	high
	backbone: nano
low	0.870	0.497	0.161
medium	0.129	0.440	0.435
high	0	0.063	0.403
	backbone: small

Table 2: Confusion matrix for quality level prediction of different YOLO backbones

Used
Com-			Yolo-Backbone
ponents
Qu	Te	Di	n	s	m	l	x
			11.6	17.7	19.8	13.9	16.2
X			4.6	1.5	2.4	2.8	1.1
	X		5.4	10.7	11.7	7.3	9.2

Table 3: Percentage of Bad Matches [%]. Best performing settings are marked in bold. The components’ abbreviations Qu , Te , and Di refer to Quality Head, Template Matching, and Swimming Direction, respectively. See also Figure 4 and Section 4.2 .

Used
Components			Scene noBG					Scene wBG
Qu	Te	Di	n	s	m	l	x	n	s	m	l	x
			22.62	18.97	17.78	16.08	18.57	20.14	18.32	18.92	19.57	18.19
X			17.87	18.75	14.11	12.11	14.16	18.78	16.84	15.86	16.38	15.19
	X		20.39	16.82	21.21	17.51	20.06	19.29	17.68	18.33	17.59	17.95
X	X		17.85	15.09	20.79	16.36	19.56	16.22	15.54	11.78	11.75	12.64

Table 4: Root Mean Squared Error of length estimation for different configurations in millimeters. Best numbers per scene are marked in bold. The components’ abbreviations Qu , Te , and Di refer to Quality Head, Template Matching, and Swimming Direction, respectively. See also Figure 4 and Section 4.2 .

Stereo Vision

Stereo vision uses images from two or more viewpoints to estimate depth, recover 3D structure, and support spatial perception.

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A Novel Computer Vision Approach for Assessing Fish Responses to Intrusive Objects in Aquaculture

Hanne-Grete Alvheim, Stian Mjelde Jakobsen, Martin Føre, Eleni Kelasidi

Department of Engineering Cybernetics, NTNU · Department of Aquaculture, SINTEF Ocean AS

Stereo Vision Aquaculture

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The aquaculture industry needs to address several challenges to secure sustainable seafood production that can serve an increasing global demand. One major challenge is to ensure good fish health and acceptable welfare during production since the improvement of fish welfare is of vital importance in current and future production systems. In this study, this is addressed by developing and implementing methods to identify fish behaviors in response to intrusive objects both on individual and on a group basis. A novel approach for detecting, tracking, and estimating the 3D position of individual fish has thus been developed, and specifically designed to track the caudal fins of farmed fish in industrial sea cages. The tracking data was subjected to a novel stereo-vision method adapted to estimate fish positions, velocities, accelerations, and turning and pitch angles. Datasets obtained from industrial-scale fish farms were then analyzed to identify the impact of structures of varying shapes, sizes, and colors on fish behavior. The method was trained using manually labeled caudal fins, and used YOLOv8 with ByteTrack as an object detector and tracker, SuperGlue for matching detections in the left and right frames, and triangulation to reconstruct the 3D positions of the fish. Different image pre-processing and augmentation methods for enhancing object detection accuracy were tested and their performance compared, while RAFT-Stereo was tested for depth estimation purposes. The obtained results both validate the method's performance against previous research efforts, and demonstrate the novelty and potential of this method in providing more insight into behavioral dynamics in sea-cages.

Abstract

Stereo Vision

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