用轻量多模态大模型实现低成本高精度电力设备缺陷分级
Lightweight Multimodal LLM-Enabled Cost-Effective Defect Grading of Power Transmission Equipment
- 基于上下文学习激活商用多模态大模型，生成高质量问答对
- 仅微调语言模型层即达当前最佳性能，3项任务均验证有效
- 适合电力巡检、工业质检等需专家经验融合的场景
电力传输设备缺陷分级（DGPTE）对电网稳定至关重要。现有机器学习方法虽具备强检测能力，却难以融合专家经验，且在更细粒度的缺陷分级中面临类别不平衡问题。本文提出一种基于多模态大语言模型（MLLM）的新型缺陷分级框架。通过上下文学习充分挖掘商用MLLM在DGPTE中的潜力，获得当前最优（SOTA）模型；随后向该模型发起二次请求，生成少量基于思维链的问答对（Q&A），显著降低人工标注成本。这些高质量、可解释的问答对用于对Qwen3-VL-8B进行基于低秩自适应的监督微调（SFT）。在三项DGPTE任务上的实验表明，仅微调语言模型层即可达到SOTA性能；多任务联合微调进一步验证了单个轻量级MLLM处理多个分级任务的可行性。
原文摘要 · Abstract (English)
Defect grading of power transmission equipment (DGPTE) is crucial to the stability of electric energy transmission. Although existing machine learning methods exhibit strong capabilities in defect detection, they are plagued by difficulties in integrating expert experience and facing class imbalance in more refined defect grading field. To address this issue, this paper introduces a novel defect grading framework based on multimodal large language model (MLLM). Specifically, this approach maximizes the commercial MLLMs' potential of DGPTE through in-context learning and obtains the state-of-te-art (SOTA) model. By sending a secondary request to this model, a small number of chain of thought-based question-answer pairs (Q\&As) are generated, which effectively reduces the cost of manual annotation. In this way, these high-quality interpretable Q\&As are used to train Qwen3-VL-8B via Low-Rank Adaption-based supervised fine-tuning (SFT). Experimental results on three DGPTE tasks demonstrate that fine-tuning only the language model layer yields the SOTA performance. Furthermore, multi-task joint fine-tuning verifies the feasibility of handling multiple grading tasks within only a single lightweight MLLM.
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。