ArticleSensors (Basel, Switzerland)2026
Cost-Effective Fish Volume Estimation in Aquaculture Using Infrared Imaging and Multi-Modal Deep Learning.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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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 cm
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