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Keywords = wood plate segmentation dataset

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16 pages, 7604 KB  
Article
WPS-Dataset: A Benchmark for Wood Plate Segmentation in Bark Removal Processing
by Rijun Wang, Guanghao Zhang, Fulong Liang, Xiangwei Mou, Bo Wang, Yesheng Chen, Peng Sun and Canjin Wang
Forests 2024, 15(12), 2076; https://doi.org/10.3390/f15122076 - 25 Nov 2024
Cited by 2 | Viewed by 1857
Abstract
Wood plate bark removal processing is critical for ensuring the quality of wood processing and its products. To address the issue of lack of datasets available for the application of deep learning methods to this field, and to fill the research gap of [...] Read more.
Wood plate bark removal processing is critical for ensuring the quality of wood processing and its products. To address the issue of lack of datasets available for the application of deep learning methods to this field, and to fill the research gap of deep learning methods in the application field of wood plate bark removal equipment, a benchmark for wood plate segmentation in bark removal processing is proposed in this study. Firstly, a costumed image acquisition device is designed and assembled on bark removal equipment to capture wood plate images in real industrial settings. After data filtering, enhancement, annotation, recording, and partitioning, a benchmark dataset named the WPS-dataset containing 4863 images was constructed. The WPS-dataset is evaluated by training six typical semantic segmentation models. The experimental results show that the models effectively learn and understand the WPS-dataset characteristics during training, resulting in high performance and accuracy in wood plate segmentation tasks. The WPS-dataset can lay a solid foundation for future research in bark removal processing and contribute to advancements in this field. Full article
(This article belongs to the Section Wood Science and Forest Products)
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