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arXiv · 2308.14583

Learning to Read Analog Gauges from Synthetic Data

Juan Leon-Alcazar·Yazeed Alnumay·Cheng Zheng·Hassane Trigui·Sahejad Patel·Bernard Ghanem
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Abstract

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.

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Juan Leon-Alcazar, Yazeed Alnumay, Cheng Zheng, Hassane Trigui, Sahejad Patel, Bernard Ghanem. 2023-08-28. Learning to Read Analog Gauges from Synthetic Data. https://arxiv.org/abs/2308.14583

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