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Automatic pollen classification and segmentation using U-nets and synthetic Data.
IEEE Access 10, 73675-73684 (2022)
Pollen allergies have become one of the most wide-spread afflictions that impact quality of life. This has made the need for automatic pollen detection, classification and monitoring a very important topic. This paper introduces a new public annotated image data-set of pollen with almost 45 thousand samples obtained from an automatic instrument. In this work we apply some of the best performing convolutional neural networks architectures on the task of pollen classification as well as some fully convolutional networks optimized for image segmentation on complex microscope images. We obtain an F1 scores of 0.95 on the new data-set when the best trained model is used as a fully convolutional classifier and a class mean Intersection over Union (IoU) of 0.88 when used as an object detector.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Baa-500 ; Convolutional Neural Networks ; Data Models ; Image Segmentation ; Licenses ; Monitoring ; Pollen Classification ; Task Analysis ; Training ; U-net
ISSN (print) / ISBN
2169-3536
e-ISSN
2169-3536
Journal
IEEE Access
Quellenangaben
Volume: 10,
Pages: 73675-73684
Publisher
IEEE
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Allergy Research (IAF)