Detection and Recognition of Pollen Grains in Multilabel Microscopic Images

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Abstract

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.

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Kubera, E., Kubik-Komar, A., Kurasiński, P., Piotrowska-Weryszko, K., & Skrzypiec, M. (2022). Detection and Recognition of Pollen Grains in Multilabel Microscopic Images. Sensors, 22(7). https://doi.org/10.3390/s22072690

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