Defect-driven quality classification of wooden lamellae using computer vision

作者：Julia Achatz, Lukas Speigl, Roman Elsener, Ingo Burgert, Andreas Rigling, Mark Schubert · 年份：2025 · DOI：10.1117/12.3063276 · 被引用次数：1 · 研究领域：Industrial Vision Systems and Defect Detection、Wood and Agarwood Research

The manual quality grading of wooden lamellae is a complex and time-consuming task, limiting productivity and consistency of industrial processes. An accurate automated sorting approach is crucial to stay competitive, optimize resource use, and adapt to future wood species. This study leverages computer vision to automate and improve quality sorting in a cost-efficient way. We collected a dataset of 5050 images from 2025 Norway spruce ( Picea abies ) lamellae captured using an industrial line scan camera. Each lamella was categorized into one of three quality classes: standard, industrial, or substandard. Based on this dataset, we developed and trained a Defect-Driven Quality Classification (DQC)—a hybrid model that uses defect segmentation and machine learning to perform the quality classification of the lamellae. An instance segmentation network (YOLOv8x) was trained to detect eight key defect types, including knots, cracks, and blue stain. To improve segmentation performance, we used cropped image patches, and a custom recombination algorithm to reconstruct full-length lamella predictions. From the resulting masks, we extracted quantitative features which were further used for classification. Additionally, we added texture features to capture the structural properties of wood. These features were used to train multiple classifiers—decision tree, random forest, gradient boosting, multilayer perceptron, and support vector machine—for quality prediction. The YOLOv8x model ach...