Fine-Grained Wood Species Identification Using Convolutional Neural Networks

Abstract

This paper considers the wood species identification from images of boards. The identification using only visual features of the surface is a challenging task even for an expert. The task becomes especially difficult when the wood species are from the same family. We propose a CNN based framework for the fine-grained classification of wood species. The framework includes a patch extraction procedure where board images are divided into image patches. Each patch is separately classified using the CNN resulting in multiple classification results per board. Finally, the patch classification results for a single board are combined. We evaluate various CNN architectures using the challenging data, consisting of species from the Pinaceae family. In addition, we propose three alternative decision rules for combining the patch classification results. By selecting a suitable amount of image patches, the proposed framework was able to achieve over 99% identification accuracy and real-time performance.