用AI自动检测木材缺陷，提升质量评估效率与一致性
Segmenting Wood Rot using Computer Vision Models
- 基于1424张图像构建数据集，融合多专家标注
- 最佳模型平均交并比达0.71，接近人工水平
- 适合木材质检、工业自动化领域应用
在木工行业中，原材料的初始质量评估需投入大量人力。本研究提出一种AI模型，用于检测、量化和定位原木缺陷，旨在自动化质量控制流程并提高评估的一致性与可靠性。为此，创建了包含1424张原木图像的数据集，由5名具有不同专业水平的标注员参与标注。通过交叉标注者一致性分析，评估了专业知识对标注结果的影响，并揭示了人为判断的主观差异。研究探索、训练并微调了当前最先进的InternImage和ONE-PEACE架构用于语义分割。最佳模型达到平均交并比（IoU）0.71，其缺陷检测与量化能力接近人类标注员水平。
原文摘要 · Abstract (English)
In 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 is 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 judgement. 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.
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