Automated Counting of Zebrafish: An Image Processing Approach

Accurate quantification of fish populations is a critical task in aquaculture and behavioral research. In this work, we present a lightweight image processing pipeline for the automatic counting of zebrafish (Danio rerio) in a multi-compartment aquatic system. The approach combines background subtraction, morphological refinement, and watershed segmentation to estimate fish counts directly from raw video without annotated training data. The method enables non-invasive and reproducible counting while remaining computationally efficient and interpretable. Although challenges remain under occlusion and limited visibility, the method reduces reliance on manual observation and demonstrates that classical image-processing techniques can provide efficient, interpretable, and accessible solutions for early-stage behavioral experiments.