Detection and Recognition of Pollen Grains in Multilabel Microscopic Images

• DOI: 10.3390/s22072690
• Publication Date: 2022-04-01T01:34:29Z
• Authors: Elżbieta Kubera; Agnieszka Kubik-K...; Paweł Kurasiński; Krystyna Piotrows...; Magdalena Skrzypiec
• Keywords: Microscopy; Chemical technology; object detection; TP1-1185; 02 engineering and technology; pollen monitoring; object detection; deep neural networks; Article; deep neural networks; pollen monitoring; 0202 electrical engineering, electronic engineering, information engineering; Pollen; Neural Networks, Computer

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.