Kavli Affiliate: Yi Zhou
| First 5 Authors: Dongwei Ji, Dongwei Ji, , ,
| Summary:
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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