Manually reading and logging gauge data is time inefficient, and the effort increases according to the number of gauges available. We present a computer vision pipeline that automates the reading of analog gauges. We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading. To facilitate the training of our approach, a synthetic dataset is generated thus obtaining a set of realistic analog gauges with their corresponding annotation. To validate our proposal, an additional real-world dataset was collected with 4.813 manually curated images. When compared against state-of-the-art methodologies, our method shows a significant improvement of 4.55 in the average error, which is a 52% relative improvement. The resources for this project will be made available at: https://github.com/fuankarion/automatic-gauge-reading.

翻译：手动读取和记录仪表数据效率低下，且随着仪表数量的增加，工作量也随之增大。我们提出了一种自动化读取模拟仪表的计算机视觉流程。该流程采用两阶段CNN管道，识别模拟仪表的关键结构组件并输出角度读数。为便于训练该方法，我们生成了一个合成数据集，从而获得一组带有相应标注的真实感模拟仪表。为验证所提方案，我们额外收集了一个包含4813张人工标注图像的真实世界数据集。与现有先进方法相比，我们的方法在平均误差上实现了4.55的显著改进，相对提升幅度达52%。该项目资源将在以下地址公开：https://github.com/fuankarion/automatic-gauge-reading。