Real-Time Classification of Rubber Wood Boards Using an SSR-Based CNN

Abstract

The classification of wood types plays an important role in many fields, especially in construction industry and furniture manufacturing. In order to manufacture rubber wood furniture with highly uniform color and texture, wood boards of different colors and textures should be classified elaborately. Many traditional methods have been applied in wood classification relying on extracting features using handcrafted descriptors designed by experienced experts, but it is not easy to construct robust features in various conditions. In this article, we present a split-shuffle-residual (SSR)-based CNN that can learn features automatically from wood images for real-time classification of rubber wood boards. Specifically, we introduce an SSR module that combines channel split and shuffle operations with residual structure to reduce the computation cost while maintaining high classification accuracy. In each module, the input is split into two low-dimensional branches, and the channel shuffle operation is used to enable the information communication between the input and the two separated branches, which is regarded as the feature reuse that enlarges network capacity without increasing complexity. The comprehensive experiments demonstrate that our algorithm outperforms other traditional classification methods and the state-of-the-art deep learning classification networks, yielding an accuracy of 94.86%. Furthermore, the analysis of running time indicates that the SSR-based CNN can be employed for wood classification in real time, which takes only 26.55 ms to handle a single image.