A multi-task strategy framework for aquaculture counting across irregular densities

Accurate monitoring of biological density is crucial for optimal breeding in aquaculture. Existing counting methods struggle to maintain precision across diverse density scenarios, ranging from sparse to dense populations. This study proposes an integrative counting framework employing a multi-task strategy tailored for Chinese soft-shelled turtles. An improved YOLOv5n is utilized for sparse scenes to enhance occlusion handling, while a novel Turtle Counting Network (TCNet) based on density map regression boosts accuracy in dense settings. Densely Connected Convolutional Networks (DenseNet) precede these counting modules, classifying image density to dynamically select the appropriate model. Experiments reveal counting accuracies of 95.04%, 96.19%, and 96.24% for low, middle, and high densities, respectively. This framework outperforms state-of-the-art methods in both sparse and dense environments, providing technological support for efficient counting under irregular densities in aquaculture.