An analysis of timber sections and deep learning for wood species classification

Abstract

The wood species classification is an essential field of investigation that can help to combat illegal logging, then providing the timber certification and allowing the application of correct timber taxing. Today, the wood classification relies on highly qualified professionals that analyze texture patterns on timber sections. However, these professionals are scarce, costly, and subject to failure. Therefore, the automation of this task using computational methods is promising. Deep learning has proven to be the ultimate technique in computer vision tasks, but it has not been much exploited to perform timber classification due to the difficulty of building large databases to train such networks. In this study, we introduced the biggest data set of wood timber microscope images to the date, with 281 species, having three types of timber sections: transverse, radial, and tangential. We investigated the use of transfer learning from pre-trained deep neural networks for wood species classification and compared their results with a state-of-art pre-designed feature method. The experimental results show that traverse section images using a densely connected network achieved 98.7% of correct classification against 85.9% of standard pre-designed features.