用多模态大模型实现电力缺陷检测的自动识别与维修闭环。
Multi-Modal Agents for Power Distribution Defect Detection: An Evaluation of Foundation Models
- 构建多模态智能体框架，融合感知、推理与工具调用能力。
- 在真实数据集上验证模型在缺陷识别与维修决策上的准确率提升。
- 适合电力巡检自动化、工业AI落地研究者参考。
配电网络是可靠供电的关键，但传统巡检方法在语义理解、泛化能力和闭环自动化方面存在局限。本文提出面向配电缺陷检测的多模态智能体框架，系统评估多模态基础模型作为统一认知引擎的性能。从三个维度进行严格评测：(1) 感知能力——准确识别设备并生成专家级缺陷描述；(2) 推理能力——结合领域知识诊断缺陷原因、评估严重程度并制定维护策略；(3) 工具使用能力——自主执行查询知识库、生成工单等操作，实现闭环运维。为此，我们构建了领域专用评估数据集与全面基准。实验结果揭示当前基础模型在三方面的优劣，为高风险工业环境中部署自主智能体提供了实证依据。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。