利用深度学习实现纤维材料显微图像中木材物种检测与分类的自动化
Automating Wood Species Detection and Classification in Microscopic Images of Fibrous Materials with Deep Learning
AI总结：
本研究构建了9个阔叶树属的离析木材参考图像数据集，提出含导管分子标注流水线的深度学习方法，实现纤维材料显微图像阔叶树种识别自动化，性能媲美人类专家，可助力森林保护。
摘要：
我们开发了一种系统生成大型离析木材参考图像数据集的方法，并用该方法生成了9个阔叶树属的图像数据。这是首次通过深度学习实现纤维材料显微图像中阔叶树种识别自动化这一重要方法的基础。我们的方法包含一个可灵活实现导管分子轻松标注的流水线。我们对比了不同神经网络架构与超参数的性能，所提方法的表现与人类专家相当。未来，这将改善全球木纤维产品流通的管控以保护森林。
英文摘要：
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
