WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929595773616128 |
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| author | Nieradzik, Lars Stephani, Henrike Sieburg-Rockel, Jördis Helmling, Stephanie Olbrich, Andrea Wrage, Stephanie Keuper, Janis |
| author_facet | Nieradzik, Lars Stephani, Henrike Sieburg-Rockel, Jördis Helmling, Stephanie Olbrich, Andrea Wrage, Stephanie Keuper, Janis |
| contents | Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images Nieradzik, Lars Stephani, Henrike Sieburg-Rockel, Jördis Helmling, Stephanie Olbrich, Andrea Wrage, Stephanie Keuper, Janis Computer Vision and Pattern Recognition Artificial Intelligence Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally. |
| title | WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2411.11738 |