Global Climate action, supported by the Paris Agreement on Climate Change, has heightened the demand for environmentally friendly materials, such as wood. However, the wood industry is not fully braced to meet this demand because of production inefficiencies caused by reliance on outdated technologies and manual labor, for instance, in wood quality inspection. The availability of Computer Vision and Deep Learning models offers an opportunity to improve efficiency in the industry. While previous studies on real-time wood defect detection using YOLO models report higher performance. These reports mainly focus on overall accuracy scores, which mask the real performance of subtle defects studied, such as live knots, which are significantly important for realworld adoption of the tested models.