Kammerbauer, RolandSchmitt, Thomas H.Bocklet, TobiasKlein, MaikeKrupka, DanielWinter, CorneliaGergeleit, MartinMartin, Ludger2024-10-212024-10-212024978-3-88579-746-3https://dl.gi.de/handle/20.500.12116/45124In the woodworking industry, a huge amount of effort has to be invested into the initial quality assessment of the raw material. In this study, we present an AI model to detect, quantify, and localize defects on wooden logs. This model aims to both automate the quality control process and provide a more consistent and reliable quality assessment. For this purpose, a dataset of 1424 sample images of wood logs is created. A total of 5 annotators possessing different levels of expertise are involved in dataset creation. An inter-annotator agreement analysis is conducted to analyze the impact of expertise on the annotation task and to highlight subjective differences in annotator judgment. We explore, train, and fine-tune the state-of-the-art InternImage and ONE-PEACE architectures for semantic segmentation. The best model created achieves an average IoU of 0.71 and shows detection and quantification capabilities close to the human annotators.enmachine learningimage segmentationsemantic segmentationInternImageONE-PEACElumberingindustrial quality controlindustrial automationText/Conference Paper10.18420/inf2024_1491617-5468