YOLO Based Intelligent Recognition of Planktonic Algae in Whole Slide Microscopic Images

Planktonic algae are widely existed biological resources in nature, and realizing real-time accurate and intelligent detection of planktonic algae is of great significance in many fields. Whole-slide scanning technology is a fundamental method to acquire and digitize microscopic images of planktonic algae, making constituting a dataset for deep learning possible. Currently, deep learning-based intelligent identification of planktonic algae is limited by the scarcity of large-scale multi-class planktonic algae dataset and the difficulty of extracting features among its various classes, which makes it hard to the detection accuracy and identification types at the same time. To solve the above problems, we propose a YOLO based method for planktonic algae recognition and analysis. In order to solve the problem of very limited samples in the dataset, we combine the mosaic and traditional data augmentation to expand the size of the dataset to ten times of the original one. To mitigate the impact of scale variations among different algal species on the proposed method, we combine the Gather-and-Distribute mechanism with the Attention Scale Sequence Fusion module, named as YOLOv8-GA, to comprehensively learning multi-scale features. We conduct experiments on our established planktonic algae dataset with 8 types, and the results show that the proposed method can get higher recognition accuracy. The accuracy rate of YOLOv8-GA for the recognition of eight types of planktonic algae is 90.1%, and the mAP@50 can be up to 92%, Compared to the unenhanced model without data augmentation, the accuracy has increased by 13.3%, while the mAP@50 has improved by 4.3%.