Automatic species identification has the potential to improve the efficacy and automation of wood processing systems significantly. Recent advances in deep learning allowed for the automation of many previously difficult tasks, and in this paper, we investigate the feasibility of using deep convolutional neural networks (CNNs) for hardwood lumber identification. In particular, two highly effective CNNs (ResNet-50 and DenseNet-121) as well as lightweight MobileNet-V2 were tested. Overall, 98.2% accuracy was achieved for 11 common hardwood species classification tasks.

中文翻译：

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深度学习基于纵剖面图像的木材识别

自动物种识别具有显着提高木材加工系统的效率和自动化的潜力。深度学习的最新进展允许许多以前困难的任务实现自动化，并且在本文中，我们研究了使用深度卷积神经网络（CNN）进行硬木木材识别的可行性。特别是，测试了两个高效的CNN（ResNet-50和DenseNet-121）以及轻巧的MobileNet-V2。总体而言，对于11种常见的硬木树种分类任务，可达到98.2％的准确性。