Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset

TL;DR

This paper introduces Pollen13K, the first large-scale microscope pollen grain image dataset with over 13,000 objects, facilitating advancements in automatic pollen classification across various scientific fields.

Contribution

It provides a comprehensive, annotated pollen image dataset and baseline classification results, enabling future research in automated pollen analysis.

Findings

Dataset contains over 13,000 pollen images.

Baseline classification results demonstrate the dataset's utility.

Methodology includes sampling, imaging, detection, segmentation, and labeling.

Abstract

Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented.