A novel quality test method of electricity meter LCD Screen based on image processing

Rui-peng, Song; Cai, Qixin; Zhong-dong, Wang; Hui-ling, SU; Shao, Xuesong; Li, Wei; Jin, Ping

doi:10.1088/1742-6596/1074/1/012167

Cited by 2 publications

(1 citation statement)

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“…Song R et al [9] propose a self-learning fast template matching method based on the sequential similarity detection algorithm (SSDA). For the sequential similarity detection algorithm, they use an adaptive threshold to replace the fixed threshold.…”

Section: Lcd Screen Defect Detectionmentioning

confidence: 99%

Automatic Detection of Display Defects for Smart Meters based on Deep Learning

Chen¹,

Hong²,

Liao³

et al. 2021

CIT. J. comput. inf. technol

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The smart meter is an essential part of an intelligent grid system. Defects in the LCD screen the smart meters affect their use. Therefore, detection of LCD screen defects of smart meters is of great significance for management and use of smart electricity meters. At present, detection methods are mainly realized by manual detection and automatic detection based on machine vision. However, performance of these two methods is not satisfactory. The fault detection task of a smart meter LCD screen can be divided into two parts: smart meter LCD localization and LCD fault detection. Therefore, this paper proposes a twostage system based on deep learning, which combines YOLOv5 with ResNet34. YOLOv5 is used for smart meter LCD localization and the classification network based on ResNet34 for LCD fault detection. We have constructed an LCD screen localization dataset and an LCD screen defect detection dataset to train and test our model. As a result, our model achieves a defect detection accuracy of 98.9% on the dataset proposed in this paper and can accurately detect the common defects of an LCD screen.

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Section: Lcd Screen Defect Detectionmentioning

confidence: 99%