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.

中文翻译：

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使用基于 SSR 的 CNN 实时分类橡胶木板

木材类型的分类在许多领域都发挥着重要作用，特别是在建筑业和家具制造中。为了制造颜色和纹理高度均匀的橡胶木家具，需要对不同颜色和纹理的木板进行精心分类。许多传统方法已经应用于木材分类，依赖于使用由经验丰富的专家设计的手工描述符提取特征，但在各种条件下构建鲁棒特征并不容易。在本文中，我们提出了一种基于 split-shuffle-residual (SSR) 的 CNN，它可以从木材图像中自动学习特征，用于橡胶木板的实时分类。具体来说，我们引入了一个 SSR 模块，该模块将通道拆分和混洗操作与残差结构相结合，以降低计算成本，同时保持较高的分类精度。在每个模块中，将输入拆分为两个低维分支，通过通道shuffle操作使输入与两个分离的分支之间进行信息通信，这被视为在不增加复杂度的情况下扩大网络容量的特征重用. 综合实验表明，我们的算法优于其他传统分类方法和最先进的深度学习分类网络，准确率为 94.86%。此外，对运行时间的分析表明，基于 SSR 的 CNN 可以实时用于木材分类，仅需 26。