Lightweight Plank Grading Based on a Multichannel Spatial Attention Mechanism

The grading and sorting process of the wood planks is a critical stage within the production line. However, in the real world, many factories still rely on humans to perform this task manually. This method is not only inefficient, but also time-consuming and labor-intensive. To solve this problem, a lightweight wood board image classification algorithm based on a multichannel spatial attention mechanism is proposed in this paper, which can be used for the real-time classification of wood planks on the production line. This method is used to classify freshly rotated cut wood planks based on defects such as damage and voids on the production line. Specifically, the received images of the wood planks were processed by a feature extraction module to effectively separate the interfering background from the foreground of the wood planks. After fusing the edge information map with the foreground image of the wood planks, a multichannel convolutional neural network with spatial and channel attention ability was used to learn the features for correctly grading the wood planks. Experimental results show that the proposed method is superior to traditional methods and some existing deep learning algorithms in terms of performance and benefits.