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Boldeanu, M.* ; González-Alonso, M.* ; Cucu, H.* ; Burileanu, C.* ; Maya-Manzano, J.M. ; Buters, J.T.M.

Automatic pollen classification and segmentation using U-nets and synthetic Data.

IEEE Access 10, 73675-73684 (2022)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Baa-500 ; Convolutional Neural Networks ; Data Models ; Image Segmentation ; Licenses ; Monitoring ; Pollen Classification ; Task Analysis ; Training ; U-net
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 2169-3536
e-ISSN 2169-3536
Zeitschrift IEEE Access
Quellenangaben Band: 10, Heft: , Seiten: 73675-73684 Artikelnummer: , Supplement: ,
Verlag IEEE
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Allergy Research (IAF)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Allergy
PSP-Element(e) G-505400-001
DOI 10.1109/ACCESS.2022.3189012
WOS ID WOS:000838496900001
Scopus ID 85134216002
Erfassungsdatum 2022-07-28
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