In many countries, the wood industry is a crucial sector and has a significant economic impact. In this sense, illegal logging is a way to reduce costs, avoiding taxes, or having access to more valuable wood species. To combat the latter, the recognition of wood species is crucial. However, this task is usually performed by experts through visual inspection, a process that requires sanding and cleaning the wood surface, and an impractical task for use in the field. In this paper, the acquisition process was simplified and a new wood dataset was introduced, where a simple pocket knife cut is used to expose the timber section for inspection. Four deep learning models with transfer learning were investigated and compared with traditional pre-designed feature methods. Additionally, the models were evaluated with a cross-validation scheme to avoid any bias. The experimental results show that deep learning outperforms pre-design features for wood classification. DenseNet achieved 98.13% of accuracy, indicating that it could be applied to assist untrained agents in wood classification.

在许多国家，木材工业是至关重要的部门，并具有重大的经济影响。从这个意义上讲，非法采伐是降低成本，避免税收或获得更有价值的木材种类的一种方法。为了与后者抗争，对木材种类的认可至关重要。但是，此任务通常由专家通过目视检查执行，该过程需要打磨和清洁木材表面，并且在现场使用时不切实际。在本文中，简化了采集过程，并引入了新的木材数据集，其中使用简单的小刀切割来暴露木材部分以进行检查。研究了四种带有迁移学习的深度学习模型，并将它们与传统的预先设计的特征方法进行了比较。此外，使用交叉验证方案评估模型，以避免任何偏差。实验结果表明，深度学习优于木材分类的预先设计功能。DenseNet达到了98.13％的准确度，表明它可用于协助未经培训的代理商进行木材分类。