迈向开放词汇工业缺陷理解：基于大规模多模态数据集的研究

摘要

我们推出IMDD-1M——首个包含100万对齐图文对的大规模工业多模态缺陷数据集，旨在推动制造业与质量检测领域的多模态学习。该数据集涵盖60余种材料类别、400多种缺陷类型的高分辨率真实缺陷样本，每个样本均配备专家核验的标注信息及描述缺陷位置、严重程度与上下文属性的细粒度文本说明。本数据集支持分类、分割、检索、描述生成和生成式建模等广泛应用。基于IMDD-1M，我们从头训练了专为工业场景定制的扩散式视觉语言基础模型。该模型作为通用化基础架构，可通过轻量级微调高效适配专业领域：仅需专用专家模型不足5%的任务特定数据，即可实现相当性能，彰显了数据高效的基础模型适配在工业检测与生成领域的潜力，为可扩展、领域自适应及知识驱动的智能制造开辟了新路径。

English

We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence.

迈向开放词汇工业缺陷理解：基于大规模多模态数据集的研究

Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset

摘要

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