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A High-Precision Automatic Pointer Meter Reading System in Low-Light Environment.

Xuang Wu^1,2, Xiaobo Shi^1,2, Yongchao Jiang¹

¹College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Sensors (Basel, Switzerland)

|July 24, 2021

Summary

This summary is machine-generated.

This study introduces an improved computer vision system for reading pointer meters, enhancing accuracy and efficiency in complex environments. The new method offers better robustness and lower training costs for automatic meter inspection.

Keywords:

automatic meter recognition illumination enhancement fusion algorithm needle direction regression skew correction

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Area of Science:

Computer Vision
Image Processing
Metrology

Background:

Pointer meters remain prevalent due to mechanical stability and electromagnetic immunity.
Manual inspection is inefficient; computer vision systems offer a promising alternative.
Existing meter image correction and recognition algorithms face challenges in robustness, training cost, and accuracy.

Purpose of the Study:

To develop a robust and accurate computer vision system for automatic pointer meter reading.
To address limitations of current algorithms, including poor robustness and high training costs.
To improve image correction and reading accuracy in complex environments.

Main Methods:

A meter image skew-correction algorithm using a binary mask and improved Mask R-CNN.
Transfer learning to reduce training costs for ellipse fitting.
A low-light enhancement fusion algorithm combining improved Retinex and Fast Adaptive Bilateral Filtering (RBF).
An improved ResNet101 for needle feature extraction and directional regression.

Main Results:

The proposed system demonstrates high accuracy in ellipse fitting for meter images.
Reduced training costs achieved through transfer learning.
Effective low-light image enhancement and correction.
Fast and accurate meter readings with improved ResNet101.

Conclusions:

The developed system offers superior efficiency and robustness for meter image correction in complex environments.
The proposed methods significantly enhance the accuracy of pointer meter readings.
This approach provides a viable and improved solution for automated meter inspection.