A Staged PEFT Framework for Industrial Pointer-Gauge Reading with Multimodal Large Language Models

Pointer gauges remain widely deployed in industrial environments because they are inexpensive, resistant to electromagnetic interference, and readable from a distance. However, automatic reading remains difficult in practice because reliable prediction requires jointly interpreting pointer geometry, scale layout, and unit-type consistency under challenging conditions such as glare, scratches, blur, and oblique viewpoints. Although multimodal large language models (MLLMs) offer a promising unified interface for visual understanding and structured output, their direct application to gauge reading is limited by weak geometric grounding, unit confusion, and unstable numeric generation. Rather than claiming a new model architecture or a new reading algorithm, this work frames the contribution as a practical adaptation and evaluation framework for applying existing MLLM and PEFT components to structured industrial gauge reading. Our framework combines three components: (i) a dedicated dataset and VQA-style annotation protocol covering multiple noise types and intensity levels; (ii) a unified screening pipeline for selecting a suitable MLLM backbone under zero-shot settings; and (iii) parameter-efficient adaptation of the selected model with Projector-LoRA, together with training and decoding mechanisms designed to improve reading robustness and output consistency. On our test set, the fine-tuned Granite-Vision 3.2 model achieves 99.9% type accuracy, 43.77% reading accuracy, and 43.60% joint accuracy. It obtains an MAE of 3.98 over valid numerical predictions, a parsing coverage of 98.65%, and an all-sample penalized normalized MAE of 0.052. These results substantially outperform the evaluated zero-shot MLLM baselines in structured prediction accuracy, although the lightweight CNN baseline remains slightly better in all-sample normalized numerical error.These results should be interpreted as evidence of promise and measurable improvement over untuned MLLMs, not as evidence that the system is already sufficient for safety-critical or fully autonomous industrial deployment. More broadly, the proposed framework offers a practical, traceable path for adapting large multimodal models to visual measurement tasks that require structured numerical outputs.