A novel deep learning algorithm for Phaeocystis counting and density estimation based on feature reconstruction and multispectral generator

Abstract

Phaeocystis proliferation is a primary instigator of algal blooms, commonly known as red tides, posing a significant threat to marine life and severely disrupting marine ecosystems. Currently, no effective method exists for estimating Phaeocystis density, underscoring an urgent need for preventative measures against Phaeocystis blooms. Given the challenges associated with the varying sizes and frequent overlapping of Phaeocystis colonies, we propose an innovative counting algorithm that leverages feature reconstruction and multispectral generator modules. Utilizing deep learning, our method achieves accurately real-time density estimation and prediction of Phaeocystis colonies. The algorithm operates in two stages: first, a multispectral reconstruction block is trained to function as a multispectral generator; second, spectral and spatial features are integrated to predict density and perform counting. Our approach surpasses existing algorithms in accuracy for Phaeocystis counting and demonstrates the utility of multispectral data in enhancing the neural network’s ability to discern targets from their background.