Detecting living microalgae in ship ballast water based on stained microscopic images and deep learning

Abstract

Motivated by the need of rapid detection of living microalgae cells in ship ballast water, this study is intended to determine the activities of microalgae using stained microscopic images and detect the living cells with image processing algorithms. The staining selectivity on living cells of neutral red dye is utilized to distinguish the activities of microalgae. A deep-learning-based detection model was designed and tested using the microscopic images of stained microalgae cells. The results showed that the deep learning model achieved high accuracies without considering the activities of microalgae: The model's average precisions (APs) on Platymonas helgolandica tsingtaoensis and Alexandrium catenella were 99.3 % and 98.3 %, respectively. In contrast, the detection accuracies of living microalgae cells were slightly lower: The model's APs on living Platymonas helgolandica tsingtaoensis and Alexandrium catenella were 91.7 % and 91.9 %, respectively. The model achieved high detection accuracy and determined the activities of microalgae cells.