Learning to Read Analog Gauges from Synthetic Data

TL;DR

This paper introduces a two-stage CNN pipeline that automates reading analog gauges using synthetic training data and real-world validation, significantly improving accuracy over existing methods.

Contribution

A novel two-stage CNN approach for reading analog gauges that leverages synthetic data for training and demonstrates superior accuracy on real-world images.

Findings

52% relative error reduction compared to state-of-the-art methods

Effective use of synthetic data for training complex gauge-reading models

Validated on a real-world dataset with nearly 5,000 images

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.