Learning to Read Analog Gauges from Synthetic Data
Juan Leon-Alcazar Yazeed Alnumay Cheng Zheng Hassane Trigui Sahejad Patel Bernard Ghanem
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.