当前位置: X-MOL 学术Ecol. Inform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
POLLEN73S: An image dataset for pollen grains classification
Ecological Informatics ( IF 5.8 ) Pub Date : 2020-10-02 , DOI: 10.1016/j.ecoinf.2020.101165
Gilberto Astolfi , Ariadne Barbosa Gonçalves , Geazy Vilharva Menezes , Felipe Silveira Brito Borges , Angelica Christina Melo Nunes Astolfi , Edson Takashi Matsubara , Marco Alvarez , Hemerson Pistori

The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology, and melissopalynology. This paper presents a new public annotated image dataset for the Brazilian Savanna called POLLEN73S composed of 2523 images from 73 pollen types. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide a baseline for pollen grain classification. Our experiments showed evidence that DenseNet-201 and ResNet-50 have superior performance against the other CNNs tested, achieving precision results of 95.7% and 94.0%, respectively. Due to its category coverage and satisfactory diversity of examples, POLLEN73S offers a diversity of pollen grain to guide progress in computer vision to solve Palynology problems.



中文翻译:

POLLEN73S:用于花粉粒分类的图像数据集

花粉种类和类型的分类是许多领域的重要任务,例如法医古生物学,考古学古生物学和褪黑粉病。本文介绍了巴西大草原的一个新的公开注释的图像数据集,称为POLLEN73S,它由来自73个花粉类型的2523张图像组成。使用最新的卷积神经网络(CNN),我们为花粉粒分类提供了基线。我们的实验表明,DenseNet-201和ResNet-50的性能优于其他测试的CNN,其精确度分别达到95.7%和94.0%。由于其类别覆盖范围和令人满意的示例多样性,POLLEN73S提供了多种花粉粒,可指导计算机视觉技术进步以解决孢粉学问题。

更新日期:2020-10-08
down
wechat
bug