Abstract

The wood manufacturing industry increasingly requires automated and intelligent systems for defect detection to ensure consistent and reliable quality control. Traditionally, this process has relied on visual inspection by human operators, which introduces variability and limits performance. This study addresses this challenge by evaluating convolutional neural networks for automatic defect classification in melamine-faced boards. Multispectral images in the visible (VIS) and near-infrared (NIR) bands were captured under real production conditions using an industrial imaging system. The Residual Network 18 and Visual Geometry Group 16 models were tested on the dataset and achieved accuracy levels comparable to those of expert human inspectors. The proposed method consistently reached over 92% accuracy across all classification tasks, indicating its practical potential for industrial quality control applications. © 2025, Universidad del Bio-Bio. All rights reserved.