Multi-Modal Agents for Power Distribution Defect Detection: An Evaluation of Foundation Models
Quan Quan
Why It Matters
What makes this one worth your time
Understanding how multimodal foundation models can be applied to industrial defect detection could enhance automation and reliability in critical infrastructure maintenance.
Evaluates multimodal foundation models for defect detection in power distribution networks.
Summary
The paper proposes a Multi-Modal Agent framework for defect detection in power distribution networks, evaluating multimodal foundation models on perception, reasoning, and tool usage capabilities. It introduces a domain-specific evaluation dataset and benchmark to assess these models' performance in identifying equipment, diagnosing defects, and executing maintenance actions.
Key contributions
- Proposal of a Multi-Modal Agent framework for defect detection.
- Systematic evaluation of multimodal foundation models in industrial settings.
- Creation of a domain-specific evaluation dataset and benchmark.
Notable insights
- The integration of perception, reasoning, and tool usage in a single framework for industrial applications.
- Development of a domain-specific evaluation dataset and benchmark for power distribution defect detection.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.12969v1 Announce Type: new Abstract: The power distribution network is critical to reliable electricity delivery, yet traditional inspection methods face limitations in semantic understanding, generalization, and closed-loop automation. To address these challenges, this paper proposes a Multi-Modal Agent framework specifically for power distribution defect detection. Central to this study is the systematic evaluation of multimodal foundation models as unified cognitive engines. We rigorously assess their integrated performance across three critical capabilities: (1) Perception, where the model must accurately identify equipment and generate expert-level descriptions of defects; (2) Reasoning, where the model interprets visual findings to diagnose causes, assess severity, and plan maintenance strategies based on domain knowledge; and (3) Tool Usage, where the model acts as an autonomous operator to execute actions -- such as querying knowledge bases or generating work orders -- to achieve closed-loop maintenance. To support this evaluation, a domain-specific evaluation dataset and a comprehensive benchmark are developed. Experimental results demonstrate the strengths and limitations of current foundation models in these three dimensions, providing empirical evidence for deploying autonomous agents in high-stakes industrial environments.