Vision meets algae: A novel way for microalgae recognization and health monitor
This work addresses marine ecological monitoring and water quality evaluation by enabling automated microalgae detection, but it is incremental as it applies existing methods to a new dataset.
The authors tackled automatic identification and location of marine microalgae in microscopy images by creating a new dataset with unbalanced classes and mixed water samples, and testing object detection models like TOOD, YOLOv5, YOLOv8, and RCNN variants, achieving high mean average precision to support real-time detection.
Marine microalgae are widespread in the ocean and play a crucial role in the ecosystem. Automatic identification and location of marine microalgae in microscopy images would help establish marine ecological environment monitoring and water quality evaluation system. We proposed a new dataset for the detection of marine microalgae and a range of detection methods, the dataset including images of different genus of algae and the same genus in different states. We set the number of unbalanced classes in the data set and added images of mixed water samples in the test set to simulate the actual situation in the field. Then we trained, validated and tested the, TOOD, YOLOv5, YOLOv8 and variants of RCNN algorithms on this dataset. The results showed both one-stage and two-stage object detection models can achieve high mean average precision, which proves the ability of computer vision in multi-object detection of microalgae, and provides basic data and models for real-time detection of microalgal cells.