Shrimp Counting Algorithm Using a Small-Scale Labeling Model

Abstract

This study presents an algorithm for shrimp counting using a small-scale labeling model. The aim is to enhance shrimp farming efficiency, reduce biosecurity risks, and minimize manual counting errors. Experimental evaluations were conducted using Litopenaeus vannamei samples divided into four growth stages. A comparison was made between the classical threshold segmentation method, small-scale labeling density estimation based on FamNet, and our proposed small-scale labeling density estimation based on FamNet-S models. The results showed that the proposed FamNet-S-based small-scale labeling density estimation method achieved a better level of accuracy than the classical FamNet model across different growth stages. In the first stage, it reduced the mean absolute error (MAE) by 8.7% and the mean squared error (MSE) by 9.6%. In the fourth stage, MAE and MSE further decreased by 18.9% and 21.6%, respectively. The research findings demonstrate that the small-scale labeling density estimation approach based on FamNet-S exhibits robust adaptability and accuracy across diverse growth stages, rendering it suitable for scenarios with limited annotated samples. It effectively tackles challenges associated with shrimp overlap and occlusion while mitigating interference from feed and excrement, thus enhancing the precision and efficiency of shrimp counting. This algorithm for small-scale labeling density estimation significantly reduces annotation workloads while facilitating rapid deployment, making it an ideal solution for counting and marking in practical aquaculture environments. The study provides a high-precision yet efficient methodology for shrimp counting and marking tasks, thus reducing labor intensity while improving farming management accuracy, thereby supporting intelligent control in shrimp farming.