用深度学习提升木材去皮机精度与效率，实测效果显著。
Development and Testing of a Wood Panels Bark Removal Equipment Based on Deep Learning
- 基于视觉系统采集图像，构建首个通用木材语义分割数据集训练BiSeNetV1模型。
- 实验室与锯木厂测试显示去皮质量与效率均明显提升。
- 适合关注工业智能检测与林业自动化升级的从业者。
将深度学习应用于木材面板去皮设备以提升去皮质量和效率是一项重要且具挑战性的任务。本研究设计并测试了一种配备视觉检测系统的木材面板去皮设备。根据锯木厂实际需求，利用视觉系统获取大量木材面板图像，首次构建了通用的木材语义分割数据集，用于训练本研究采用的BiSeNetV1模型。同时详细阐述了去皮过程中关键数据的计算方法与处理流程。在实验室和锯木厂环境中分别进行了BiSeNetV1模型的对比实验及去皮效果测试。结果表明，应用BiSeNetV1分割模型合理可行；去皮效果测试结果显示去皮质量与效率均有显著提升。所开发设备完全满足锯木厂对去皮加工精度与效率的要求。
原文摘要 · Abstract (English)
Attempting to apply deep learning methods to wood panels bark removal equipment to enhance the quality and efficiency of bark removal is a significant and challenging endeavor. This study develops and tests a deep learning-based wood panels bark removal equipment. In accordance with the practical requirements of sawmills, a wood panels bark removal equipment equipped with a vision inspection system is designed. Based on a substantial collection of wood panel images obtained using the visual inspection system, the first general wood panels semantic segmentation dataset is constructed for training the BiSeNetV1 model employed in this study. Furthermore, the calculation methods and processes for the essential key data required in the bark removal process are presented in detail. Comparative experiments of the BiSeNetV1 model and tests of bark removal effectiveness are conducted in both laboratory and sawmill environments. The results of the comparative experiments indicate that the application of the BiSeNetV1 segmentation model is rational and feasible. The results of the bark removal effectiveness tests demonstrate a significant improvement in both the quality and efficiency of bark removal. The developed equipment fully meets the sawmill's requirements for precision and efficiency in bark removal processing.
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