VNWoodKnot: A High-Quality Image Dataset for Wood Knot Detection and Classification

Timber knot detection is essential for automated grading and quality control in the wood processing industry. Knots, which arise at the intersection of branches and the tree trunk, are among the most influential defects affecting both structural integrity and aesthetics. This paper introduces VNWoodKnot, a publicly available image dataset comprising 1,515 high-resolution wood surface images, collected in a Vietnamese industrial facility. The dataset includes three categories: live knots (519 images), dead knots (496 images), and knot-free surfaces (500 images). Each image was captured under diverse lighting and angle conditions and manually annotated with bounding boxes. Live knots are structurally integrated and color-consistent, while dead knots are darker, cracked, and loosely attached. VNWoodKnot enables both classification and object detection task and addresses a critical gap in publicly accessible datasets for AI-driven wood defect inspection. It serves as a crucial benchmark for the development of real-time, scalable, and reliable deep learning models for industrial-grade wood defect inspection.

木材节子检测是木材加工行业实现自动化分级与质量管控的核心环节。节子作为树枝与树干交汇处形成的缺陷，是同时影响木材结构完整性与外观质感的主要缺陷类型之一。本研究提出VNWoodKnot数据集：这是一个公开可用的木材表面图像数据集，共包含1515张高分辨率图像，采集自越南某工业生产现场。该数据集涵盖三个类别：活节（519张）、死节（496张）以及无节表面（500张）。所有图像均在多样化的光照与拍摄角度条件下采集，并通过人工标注得到边界框。活节与木材本体结构融合且色彩保持一致；死节则颜色更深、存在开裂现象且与基材结合松散。VNWoodKnot可同时支持分类与目标检测两类任务，填补了当前AI驱动木材缺陷检测领域公开可用数据集的关键空白，可为面向工业级木材缺陷检测的实时、可扩展且可靠的深度学习模型研发提供重要基准测试平台。