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Summary

This study presents GAUREAD, a computer vision system for reading analog gauges autonomously. It overcomes limitations of existing software by eliminating manual input and enabling readings from various angles, improving automation in robotics.

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analogue gaugesautonomous measurementscomputer visiondetection systemsinfrastructures protections

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Area of Science:

  • Computer Vision
  • Robotics
  • Measurement Systems

Background:

  • Existing gauge reading software often requires manual input for gauge location and scale.
  • Current systems typically only function with a direct, frontal view of the gauge.
  • Autonomous gauge reading in unstructured environments remains a challenge.

Purpose of the Study:

  • To introduce GAUREAD, an end-to-end computer vision system for autonomous analog gauge reading.
  • To develop a system that operates without manual input or predefined gauge parameters.
  • To enable gauge measurement in unstructured environments and from varied perspectives.

Main Methods:

  • GAUREAD integrates gauge detection, perspective rectification, and scale reconstruction.
  • The system processes images to identify and interpret analog gauges.
  • The algorithm is optimized for real-time performance on embedded systems like the NVIDIA Jetson Nano.

Main Results:

  • GAUREAD achieves autonomous gauge reading with high accuracy.
  • Measurement error is within 3% for perspective angles up to 20° and 9% up to 50°.
  • The system demonstrates a reading speed of 800 milliseconds per gauge on an NVIDIA Jetson Nano.

Conclusions:

  • GAUREAD offers a robust solution for autonomous analog gauge reading in complex environments.
  • The system's ability to handle varied perspectives and unstructured data enhances its applicability.
  • Potential applications include automating safety and security operations in mobile robotics.