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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.11051 |
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| _version_ | 1866929327796387840 |
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| author | Wang, Rijun Zhang, Guanghao Liang, Fulong Wang, Bo Mou, Xiangwei Chen, Yesheng Sun, Peng Wang, Canjin |
| author_facet | Wang, Rijun Zhang, Guanghao Liang, Fulong Wang, Bo Mou, Xiangwei Chen, Yesheng Sun, Peng Wang, Canjin |
| contents | Using deep learning methods is a promising approach to improving bark removal efficiency and enhancing the quality of wood products. However, the lack of publicly available datasets for wood plate segmentation in bark removal processing poses challenges for researchers in this field. To address this issue, a benchmark for wood plate segmentation in bark removal processing named WPS-dataset is proposed in this study, which consists of 4863 images. We designed an image acquisition device and assembled it on a bark removal equipment to capture images in real industrial settings. We evaluated the WPS-dataset using six typical segmentation models. The models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. We believe that our dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11051 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing Wang, Rijun Zhang, Guanghao Liang, Fulong Wang, Bo Mou, Xiangwei Chen, Yesheng Sun, Peng Wang, Canjin Computer Vision and Pattern Recognition Using deep learning methods is a promising approach to improving bark removal efficiency and enhancing the quality of wood products. However, the lack of publicly available datasets for wood plate segmentation in bark removal processing poses challenges for researchers in this field. To address this issue, a benchmark for wood plate segmentation in bark removal processing named WPS-dataset is proposed in this study, which consists of 4863 images. We designed an image acquisition device and assembled it on a bark removal equipment to capture images in real industrial settings. We evaluated the WPS-dataset using six typical segmentation models. The models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. We believe that our dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field. |
| title | WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.11051 |