PB-IAD：利用多模态基础模型在动态制造环境中进行语义工业异常检测
PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments
AI总结：
针对动态制造环境中工业异常检测的数据依赖、灵活性不足问题，提出PB-IAD框架，利用基础模型多模态与推理能力，通过提示模板和预处理模块解决数据稀疏等需求，在GPT-4.1上评估并超越PatchCore等方法，尤其在数据稀疏场景表现优异。
摘要：
制造过程中的异常检测对于确保产品质量和识别过程偏差至关重要。统计和数据驱动方法仍是工业异常检测的标准，但它们的适应性和可用性受到对大量标注数据集的依赖以及动态生产条件下灵活性有限的制约。基础模型感知能力的最新进展为其适应这一下游任务提供了有前景的机会。本文提出PB-IAD（基于提示的工业异常检测），这是一种利用基础模型的多模态和推理能力进行工业异常检测的新型框架。具体而言，PB-IAD解决了动态生产环境的三个关键需求：数据稀疏性、敏捷适应性和以领域用户为中心。除异常检测外，该框架还包括一个专门设计用于迭代实施领域特定过程知识的提示模板，以及一个将领域用户输入转换为有效系统提示的预处理模块。这种以用户为中心的设计允许领域专家灵活定制系统，而无需数据科学专业知识。所提出的框架通过在三个不同制造场景、两种数据模态和一项消融研究中使用GPT-4.1进行评估，以系统地评估语义指令的贡献。此外，PB-IAD与PatchCore等最先进的异常检测方法进行了基准测试。结果表明，仅通过语义指令即可实现卓越性能，尤其是在数据稀疏场景和少样本设置中。
英文摘要：
The detection of anomalies in manufacturing processes is crucial to ensure product quality and identify process deviations. Statistical and data-driven approaches remain the standard in industrial anomaly detection, yet their adaptability and usability are constrained by the dependence on extensive annotated datasets and limited flexibility under dynamic production conditions. Recent advances in the perception capabilities of foundation models provide promising opportunities for their adaptation to this downstream task. This paper presents PB-IAD (Prompt-based Industrial Anomaly Detection), a novel framework that leverages the multimodal and reasoning capabilities of foundation models for industrial anomaly detection. Specifically, PB-IAD addresses three key requirements of dynamic production environments: data sparsity, agile adaptability, and domain user centricity. In addition to the anomaly detection, the framework includes a prompt template that is specifically designed for iteratively implementing domain-specific process knowledge, as well as a pre-processing module that translates domain user inputs into effective system prompts. This user-centric design allows domain experts to customise the system flexibly without requiring data science expertise. The proposed framework is evaluated by utilizing GPT-4.1 across three distinct manufacturing scenarios, two data modalities, and an ablation study to systematically assess the contribution of semantic instructions. Furthermore, PB-IAD is benchmarked to state-of-the-art methods for anomaly detection such as PatchCore. The results demonstrate superior performance, particularly in data-sparse scenarios and low-shot settings, achieved solely through semantic instructions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
