Abstract
Wood is a renewable and widely used material whose mechanical performance is strongly influenced by natural defects such as knots, cracks, and resin pockets. These defects affect structural accuracy and make quality assessment an essential step in wood processing and engineering applications. Traditional visual inspection methods are time consuming and subject to individual variability, motivating the development of automated inspection systems based on computer vision. This study proposes a YOLOv8-based framework for automated wood classification and specimen selection. Two YOLOv8 variants, Nano and Medium, were trained on a publicly available wood defect dataset containing more than 20,000 images and over 43,000 annotated defects. The models were used to distinguish defect-free specimens from specimens containing visible defects, enabling objective sample selection. Only specimens classified as defect-free were retained for subsequent three-point bending tests and finite element method (FEM) simulations. Both YOLOv8 models achieved reliable detection performance, allowing consistent separation of defective and defect-free wood samples. The selected specimens exhibited stable mechanical behavior during bending tests, while FEM predictions showed good agreement with the experimental results, with errors below 3.2% in the elastic range. The proposed methodology demonstrates the potential of integrating artificial intelligence, mechanical testing, and numerical simulation into a unified workflow for wood quality assessment and structural evaluation.