WPS-Dataset: A benchmark for wood plate segmentation in bark removal
  processing
Rijun Wang Guanghao Zhang Fulong Liang Bo Wang Xiangwei Mou Yesheng Chen
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.