AI 中文总结
针对模拟仪表读数中数据分散且隐私受限的问题，提出联邦学习框架，通过四阶段流水线实现多站点协作训练，并发布带掩膜标注的 MeterFL 数据集以支持系统评估。
AI 中文摘要
模拟表盘仪表广泛应用于工业应用和公用事业站点，这些场景中环境和仪表类型各异，且检查数据可能敏感。目前，实践中必须针对每种环境和仪表类型单独开发和部署自动抄表器。深度学习能够处理这种变异性，但需要多样化的标注数据，而这些数据的收集和更新成本高昂。实际上，仪表图像分布在各个独立站点，每个站点的标注有限，同时由于所有权、治理或隐私限制，原始图像通常无法集中汇总。为应对这些挑战，我们提出了一种用于视觉模拟仪表读数的联邦学习框架，使多个站点能够在无需共享原始图像的情况下协作训练读数模型。我们的框架包含四个阶段的流水线：(1) 表盘定位，(2) 跨客户端联邦训练的细结构分割，(3) 极坐标展开，以及 (4) 用于最终读数的刻度计数解码。为支持对该设置的系统性评估，我们发布了 MeterFL，一个包含 1,382 张带掩膜标注图像的数据集，该数据集通过确定性规则根据视觉属性组织成由部署动机驱动的伪客户端，并在分割训练和测试划分之间进行 dHash 近重复控制。我们评估了分割质量和端到端读数准确性。MeterFL 可在该 https URL 公开获取。
英文摘要
Analog dial meters are widely deployed in industrial applications and utility sites, where environments and meter types vary and inspection data may be sensitive. Currently, automatic meter readers must be individually developed and deployed for each environment and meter type in practice. Deep learning could handle this variability but requires diverse labeled data that are costly to collect and update. In practice, meter images are distributed across independent sites, each with limited labels, while raw images often cannot be pooled because of ownership, governance, or privacy constraints. To address these challenges, we present a federated framework for visual analog meter reading that enables multiple sites to collaboratively train a reading model without sharing their raw images. Our framework consists of a four-stage pipeline: (1) dial localization, (2) thin-structure segmentation trained federatively across clients, (3) polar unwrapping, and (4) tick-counting decoding for final reading. To enable systematic evaluation of this setting, we release MeterFL, a 1,382-image mask-annotated dataset organized into deployment-motivated pseudo-clients derived from visual attributes via deterministic rules, with dHash near-duplicate control between the segmentation train and test splits. We evaluate both segmentation quality and end-to-end reading accuracy. MeterFL is publicly available at https://github.com/weidazhaoooo/Meter-FL.
