用视觉语言模型直接读取模拟仪表，无需复杂标注流程。
Vision-Language Models for Analog Gauge Reading: An Empirical Study of Specialization, Transfer and Reliability
- 用QLoRA微调通用视觉语言模型，实现零样本到小样本的直接读数。
- 合成数据上误差低至2.39%，工业数据上为4.43%。
- 模型对模糊敏感，高置信度仍可能出错，适合安全关键场景需验证。
模拟仪表在工业环境中仍广泛存在，人工读数成本高且危险。本文研究直接从单目标仪表图像中读取数值的任务，贡献在于系统评估通用视觉语言模型（VLM）在专业化、迁移性、鲁棒性和可靠性方面的表现，无需显式的指针分割或几何读数流程。采用Qwen2.5-VL-7B-Instruct模型，在公开合成数据集、视频生成的Pressure Gauge数据集及专有工业数据集上，通过零样本提示、上下文学习（ICL）和量化低秩适应（QLoRA）进行参数高效微调。所有微调实验均采用固定20轮协议，最后一轮用于分析；对比了有无提供量程的模型以排除提示干扰。主要指标为归一化均百分比误差（MPE）。最佳微调结果：合成数据集上为2.39%（95%置信区间1.43-3.90%），Pressure Gauge数据集上为2.61%（CI 1.66-3.80%），专有工业数据集上为4.43%（CI 2.31-7.14%）。留一数据集实验显示显著迁移性能下降，鲁棒性测试表明高斯模糊是最强破坏因素。可靠性分析发现高置信度下仍可能发生错误，推动在安全关键场景中引入拒绝回答与独立验证机制。结果支持基于QLoRA的专用VLM用于单仪表读数，但尚不足以部署为完整工厂监控系统。
原文摘要 · Abstract (English)
Analog gauges remain common in industrial environments where manual inspection is costly or hazardous. The engineering application addressed here is direct numerical reading of single-target analog-gauge images, while the artificial-intelligence contribution is a systematic evaluation of specialization, transfer, robustness and reliability for a general-purpose vision-language model (VLM) without an explicit pointer-segmentation and geometric-reading pipeline. The Qwen2.5-VL-7B-Instruct model is evaluated using zero-shot prompting, in-context learning (ICL) and parameter-efficient fine-tuning with Quantized Low-Rank Adaptation (QLoRA) on a public synthetic dataset, a video-derived Pressure Gauge dataset and a proprietary industrial dataset. All fine-tuning experiments use a fixed 20-epoch protocol with the final epoch used for analysis; separate models with and without supplied gauge ranges remove prompt-setting confounds. The primary metric is range-normalized mean percentage error (MPE). The best fine-tuned MPE values are 2.39% on the synthetic dataset, with a 95% bootstrap confidence interval (CI) of 1.43-3.90%; 2.61% on the Pressure Gauge dataset, with a CI of 1.66-3.80%; and 4.43% on the proprietary industrial dataset, with a CI of 2.31-7.14%. Leave-one-dataset-out experiments reveal substantial transfer degradation on held-out synthetic and proprietary data, while robustness tests identify Gaussian blur as the strongest tested corruption. Reliability analysis shows that high-confidence errors remain possible, motivating abstention and independent validation in safety-critical use. These results support QLoRA-specialized VLMs for direct single-gauge reading but not yet a deployment-ready plant-monitoring pipeline.
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