Wood surface image classification using multi-attention graph Neural Networks

This study aims to address the challenge of detecting wood surface defects with high accuracy in real-world manufacturing environments, which are affected by various conditions. The goal is to build a hybrid deep learning model that can operate effectively in real time, serving automated quality control in wood processing. The research team proposes a hybrid architecture that combines multiple models: You Only Look Once – version 10 and CenterNet for accurate and fast localization of defect areas, Graph Attention Network to exploit spatial relationships between features, and Multilayer Perceptron for the final classification stage. At the same time, the Battle Royale Optimization algorithm is applied to select the optimal feature. The model is trained and tested on a large-scale dataset of 20,275 high-resolution wood surface images, covering 10 different types of defects. Experiments show that the model achieves a classification accuracy of 92.7% and a processing speed of 40 fps, meeting the requirements of real-time industrial systems. The model also demonstrates good generalization ability when effectively detecting both obvious defects and subtle abnormalities. The proposed hybrid modeling framework not only improves the efficiency of wood surface defect detection but also provides an automated, robust, and highly scalable solution for quality control in smart manufacturing. This contributes to optimizing the use of raw materials, minimizing waste, and enhancing the competitiveness of the wood processing industry.