A comprehensive review of automatic defect detection in wooden surface inspection

This review focused on automated detection and classification of wood surface defects, such as knots, cracks, and resin pockets. Machine learning and deep learning methods are increasingly replacing traditional inspection techniques, such as manual checks and imaging tools like X-rays and ultrasound. However, these conventional approaches suffer from significant limitations in accuracy and efficiency. We reviewed both single-stage models, such as you only look once (YOLO) and its variants, and two-stage models like faster region-based convolutional neural networks (R-CNN), and examined their strengths, limitations, and relevance in real industrial applications. We further investigated emerging zero-shot learning approaches that use vision-language models and NLP techniques to detect previously unseen wood defects. Zero-shot models do not rely on annotated training data. These methods offer scalable and flexible solutions, especially in scenarios where collecting labelled samples for all defect types is impractical. The paper also discussed publicly available labelled datasets used to train and test these models, and discussed standard performance metrics such as precision, recall, and mean average precision. This review aimed to support further research and practical improvements in automated wood surface quality assessment by analyzing defect types, detection methods, and evaluation techniques.