Detection and Recognition of Pollen Grains in Multilabel Microscopic Images

Analysis of pollen material obtained from the Hirst-type apparatus, which is a tedious and labor-intensive process, is usually performed by hand under a microscope by specialists in palynology. This research evaluated the automatic analysis of pollen material performed based on digital microscopic photos. A deep neural network called YOLO was used to analyze microscopic images containing the reference grains of three taxa typical of Central and Eastern Europe. YOLO networks perform recognition and detection; hence, there is no need to segment the image before classification. The obtained results were compared to other deep learning object detection methods, i.e., Faster R-CNN and RetinaNet. YOLO outperformed the other methods, as it gave the mean average precision (mAP@.5:.95) between 86.8% and 92.4% for the test sets included in the study. Among the difficulties related to the correct classification of the research material, the following should be noted: significant similarities of the grains of the analyzed taxa, the possibility of their simultaneous occurrence in one image, and mutual overlapping of objects.

Automated summary

This research evaluated the automatic analysis of pollen material based on digital microscopic photos using a deep neural network called YOLO. The results showed that YOLO outperformed other deep learning methods in terms of detection and recognition, with mean average precision ranging from 86.8% to 92.4% for the test sets. The study also identified difficulties in correctly classifying pollen material, such as the similarities and overlapping of grains.