Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
- We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexibleWe have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.…
Document Type: | Article (unreviewed) |
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Zitierlink: | https://opus.hs-offenburg.de/8403 | Bibliografische Angaben |
Title (English): | Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning |
Author: | Lars Nieradzik, Jördis Sieburg-Rockel, Stephanie Helmling, Janis KeuperStaff MemberORCiDGND, Thomas Weibel, Andrea Olbrich, Henrike Stephani |
Year of Publication: | 2023 |
First Page: | 1 |
Last Page: | 17 |
DOI: | https://doi.org/10.48550/arXiv.2307.09588 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Institutes: | Bibliografie |
Tag: | EU Timber Regulation; deep learning; maceration, vessel elements; wood identification | Formale Angaben |
Relevance: | Keine Relevanz |
Open Access: | Open Access |
Diamond | |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
Comment: | Preprint |
ArXiv Id: | http://arxiv.org/abs/2307.09588 |