Computer Vision System for Image-based Automated Dial Meter Reading

Devyatkin, A. V.; Muzalevskiy, Alexander R.; Morozov, Alexander S.

doi:10.1109/scm55405.2022.9794897

Cited by 2 publications

(1 citation statement)

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“…By utilizing the latest generative neural network model, the generated numbers resemble the local appearance of those in the original meter images. Devyatkin et al [23] introduced a computer vision system that can recognize the measurements of dial meter devices attained by video camera, in the paper, the authors took into account the convenience of end users, described the operation algorithm, and gave the recognition results. To solve the instability of conventional meter inspection methods, Li et al [24] proposed a automatic reading method for analog meters based on YOLOv5s detector.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Recognizing Readings in Images of Electric Circuit Inspection Meters Based on Deep Learning

Xia¹,

Zhang²,

Zheng³

et al. 2023

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For electric circuit inspection, the conventional manual inspection method has a series of problems including the heavy work load of recording the readings, the low accuracy, and the hidden safety hazards. The intelligent recognization method for images of meter readings based on digital image technology has a great practical value. However, the existing recognization methods for meter readings based on deep learning generally ignore the extraction of key points such as the pointer and scale on meter dashboard, the existing algorithms are of poor robustness and anti-jamming ability, therefore, this paper aims to study a novel method for recognizing readings in the images of electric circuit inspection meters based on deep learning. At first, this paper corrects the tilt of meter dashboard, and accurately positions the dashboard center. Then, based on the YOLOv5 network model, this paper constructs the said recognization model, gives the structure of the YOLOv5 network model, and introduces its working principle. At last, experimental results are drawn to verify the validity of the proposed method for processing the images of meter readings and the constructed recognization model.

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Section: Introductionmentioning

confidence: 99%