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.

利用深度学习方法可以提高去皮效率，增强木制品质量。本研究提出了一种用于去皮过程中的木板分割的基准数据集 WPS-dataset，共包含 4863 幅图像。通过六种典型的分割模型对 WPS-dataset 进行评估，模型在训练过程中有效地学习和理解 WPS-dataset 的特征，从而在木板分割任务中表现出高性能和准确性。我们相信这个数据集可以为未来去皮处理的研究奠定坚实的基础，并对该领域的发展做出贡献。