用分步架构实现风机叶片缺陷自动定位与报告生成，效果优于通用模型。
A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection
- 分三模块：检测、空间编码、生成，支持边缘部署
- 报告生成质量高，幻觉率仅4%，专家评分8.6/10
- 小模型+领域微调胜过大模型，适合工业落地
自动化工业检测需精确缺陷定位与结构化维护报告生成，当前流程将两者分离，语言解释依赖人工。本文提出一种解耦的边缘部署流水线，用于风力涡轮机叶片检测，包含三个组件：检测器采用YOLO26-x-obb在原图分辨率下定位缺陷；桥接模块为无参编码器，将每个边界框映射为网格参考的空间令牌，嵌入结构化提示；大脑模块使用4比特量化后的Qwen-2.5-1.5B模型，通过QLoRA在947条合成维护报告上微调，从该提示生成结构化JSON报告；检索增强微调（RAFT）使每项建议基于索引的维护规程。五组消融实验对比了完整系统与单体视觉语言模型（VLM）基线及部分移除组件的配置，结果表明完整系统在BLEU-4达0.41，幻觉率（HR）为4%，专家评分8.6/10，而零样本VLM基线仅为0.07、65%和3.3/10。经相同检测证据，该1.5B模型生成报告质量高于671B参数通用API模型，且在单张T4 GPU上达47词元/秒。结果表明，针对任务设计的解耦架构结合小规模领域语料，优于通用端到端模型。
原文摘要 · Abstract (English)
Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these tasks are handled separately, with linguistic interpretation left to human experts. This paper describes a decoupled, edge-deployable pipeline for wind turbine blade inspection built from three components that each handle a distinct sub-task. The Eyes a YOLO26-x-obb oriented bounding-box detector localizes defects at dataset-native resolution. The Bridge a deterministic, parameter-free encoding module maps each detected bounding box to grid-referenced spatial tokens embedded in a structured prompt. The Brain a 4-bit quantized Qwen-2.5-1.5B model adapted with Quantized Low-Rank Adaptation (QLoRA) on 947 synthetically generated maintenance reports generates a structured JSON report from that prompt. Retrieval-Augmented Fine-Tuning (RAFT) further grounds each recommendation in indexed maintenance procedures. Five ablation experiments, scored by BLEU-4, ROUGE-L, Hallucination Rate (HR), and an LLM-as-a-Judge rubric, compare the pipeline against a monolithic vision-language model (VLM) baseline and against partial configurations in which one component is removed. The complete system achieves BLEU-4 0.41, HR=4%, and Expert Score = 8.6/10 compared with 0.07, 65%, and 3.3/10 for the zero-shot VLM baseline. The QLoRA-adapted 1.5B model generates higher-quality reports than a 671B-parameter generalist API model given identical detection evidence, at 47 tokens per second on a single T4-class GPU. The results show that purpose-built decoupled architecture with a small domain-specific training corpus outperforms a generalist end-to-end model on this structured generation task.
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