Granulo-10K: A Large-Scale Benchmark Dataset for Multiple-View Industrial Granulometry

Granulometric analysis of wood strands ensures structural integrity and production efficiency of Oriented Strand Board (OSB). Current vision-based studies have demonstrated the potential of automated image analysis for particle size estimation, but their progress is significantly hindered by the absence of public, high-quality, and domain-specific datasets. This paper introduces Granulo-10k, the first curated, open dataset of multiple-view wood-strand imagery designed specifically for research on OSB strand segmentation and multiple-view granulometry, covering height, width, and thickness. The dataset includes about 10, 000 high-resolution images of 200 wood strands captured under controlled acquisition conditions, along with granulometric ground truth. We also provide 3D point clouds to capture three-dimensional information. We describe the acquisition protocol and annotation methodology used to ensure representativeness across real production variability. Baseline evaluations using modern deep neural networks and foundation models demonstrate the dataset’s utility for benchmarking while revealing open research challenges such as precise thickness estimation.