Application of variant transfer learning in wood recognition

Abstract

Wood is a material commonly found in nature and is widely used in all professions and industries. Because wood has varied growth cycles and physical properties, there are large differences in its usage and commercial price. In addition, some woods are nationally protected species. Therefore, it is of great importance to accurately identify the type of wood. Traditional wood recognition methods rely on experts and specialized equipment. To facilitate wood recognition, this paper proposes an approach for wood recognition using images. Next, a transfer learning technology was used to extract the textural features of wood, and a global average pooling (GAP) layer was used to reduce the number of features. Finally, the extreme learning machine (ELM) was used for classification. The recognition accuracy of this approach for the Wood Species Dataset was 93.07%, which was higher than the method used by the data provider. This approach had a higher recognition accuracy and a more stable recognition performance than previous approaches.