WPS-Dataset: A benchmark for wood plate segmentation in bark removal processing
This provides a benchmark dataset for researchers in wood processing to improve bark removal efficiency and product quality, but it is incremental as it focuses on a new dataset for an existing application.
The authors tackled the lack of publicly available datasets for wood plate segmentation in bark removal processing by creating the WPS-Dataset, which consists of 4863 images captured in real industrial settings, and they evaluated it using six segmentation models, achieving high performance and accuracy.
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.