Comparative Study of YOLO Architectures for Automated Wood Defect Detection

Abstract

The North American residential construction industry relies on wood as the principal material for structural framing, as well as for kitchen cabinets, decorative trim moldings, and door casings. In this regard, wood quality is a key determinant of structural integrity and aesthetics in construction. Traditional wood defect inspection during construction and furniture manufacture is time-consuming, inconsistent, and error prone. Machine-vision technology could solve these issues and improve wood quality assessment. The use of automated defect detection systems can improve inspection efficiency and accuracy while reducing manual labour. This paper evaluates the performance of four advanced You Only Look Once (YOLO) object detection models: YOLOv5l-seg, YOLOv7-E6E, YOLOv8l, and YOLOv9e for automated wood defect identification. Each variant involves a balance between accuracy and computational efficiency. YOLOv5l-seg supports segmentation, YOLOv7-E6E improves feature extraction, YOLOv8l speeds up inference, and YOLOv9e uses transformer-based components for better detection. Using a dataset of 3,300 annotated images spanning ten defect types, it was found that YOLOv9e achieves the highest precision (90.15%), demonstrating strong potential for real-time wood inspection in construction and manufacturing workflows. The results are discussed in the context of their applicability to off-site construction systems for quality tracking and defect traceability.