AutoIAD自动构建工业异常检测模型，省去人工干预。
AutoIAD: Manager-Driven Multi-Agent Collaboration for Automated Industrial Anomaly Detection
- 用管理型主代理协调多个专业子代理完成全流程
- 在MVTec数据集上任务完成率和AUROC显著提升
- 适合制造业质量控制场景，减少人工依赖
工业异常检测对制造质量控制至关重要，但传统方法需大量人工适配不同场景。本文提出AutoIAD，一个面向端到端自动化工业视觉异常检测的多智能体协作框架。该框架通过管理型中央代理协调数据准备、数据加载、模型设计、训练等专用子代理，并集成领域知识库，利用原始工业图像数据自动构建训练好的异常检测模型。我们基于MVTec AD数据集构建了全面基准，评估AutoIAD在多种LLM后端下的表现。大量实验表明，AutoIAD在任务完成率和模型性能（AUROC）上显著优于现有通用智能体协作框架与传统AutoML框架，同时通过迭代优化有效缓解幻觉问题。消融实验进一步验证了管理代理与领域知识库模块在生成鲁棒、高质量工业异常检测方案中的关键作用。
原文摘要 · Abstract (English)
Industrial anomaly detection (IAD) is critical for manufacturing quality control, but conventionally requires significant manual effort for various application scenarios. This paper introduces AutoIAD, a multi-agent collaboration framework, specifically designed for end-to-end automated development of industrial visual anomaly detection. AutoIAD leverages a Manager-Driven central agent to orchestrate specialized sub-agents (including Data Preparation, Data Loader, Model Designer, Trainer) and integrates a domain-specific knowledge base, which intelligently handles the entire pipeline using raw industrial image data to develop a trained anomaly detection model. We construct a comprehensive benchmark using MVTec AD datasets to evaluate AutoIAD across various LLM backends. Extensive experiments demonstrate that AutoIAD significantly outperforms existing general-purpose agentic collaboration frameworks and traditional AutoML frameworks in task completion rate and model performance (AUROC), while effectively mitigating issues like hallucination through iterative refinement. Ablation studies further confirm the crucial roles of the Manager central agent and the domain knowledge base module in producing robust and high-quality IAD solutions.
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