# Cost-Effective Fish Volume Estimation in Aquaculture Using Infrared Imaging and Multi-Modal Deep Learning

## TL;DR

This paper introduces a low-cost infrared imaging system for estimating fish volume in aquaculture, enabling efficient and sustainable biomass monitoring.

## Contribution

The novel pipeline uses infrared-only imaging with multi-modal deep learning to achieve high accuracy at a fraction of the cost of existing methods.

## Key findings

- The system achieves a Mean Absolute Error of 0.85 cm³ and an R² of 0.961 for fish volume estimation.
- It outperforms state-of-the-art methods by 19–41% while reducing hardware costs by 80%.
- The pipeline includes five modules for IR-to-depth estimation, IR-to-RGB generation, detection, segmentation, and volume estimation.

## Abstract

Accurate fish volume estimation is essential for sustainable aquaculture management, yet traditional methods are invasive and costly, while existing non-invasive approaches rely on expensive multi-sensor setups. This study proposes a cost-effective infrared (IR)-only pipeline that reconstructs depth and Red Green Blue (RGB) from low-cost infrared videos (<USD 100 per camera), enabling scalable biomass monitoring in dense tanks. The pipeline integrates five modules: IR-to-depth estimation with contour-guided attention and smoothing loss; IR-to-RGB generation via texture-conditioned injection and water-adaptive loss; detection and tracking using cross-modal fusion and behavior-constrained Kalman filtering; instance segmentation with depth-guided branches and deformation-adaptive loss; and volume estimation through trajectory–depth Transformer fusion with refraction correction. Trained on a curated dataset of 166 goldfish across 124 videos (8–16 fish/tank), the system achieves Mean Absolute Error (MAE) of 0.85 cm3 and coefficient of determination (R2) of 0.961 for volume estimation, outperforming state-of-the-art methods by 19–41% while reducing hardware costs by 80%. This work advances precision aquaculture by providing robust, deployable tools for feed optimization and health monitoring, promoting environmental sustainability amid rising global seafood demand.