Izvorni znanstveni članak
https://doi.org/10.20532/cit.2020.1005158
Automatic Detection of Display Defects for Smart Meters based on Deep Learning
Ye Chen
; Electric Power Research Institute of Yunnan, China
Zhihu Hong
; Electric Power Research Institute of Yunnan, China
Yaohua Liao
; Electric Power Research Institute of Yunnan, China
Mengmeng Zhu
; Electric Power Research Institute of Yunnan, China
Tong Han
; Electric Power Research Institute of Yunnan, China
Quan Shen
; Beijing University of Posts and Telecommunications, China
Sažetak
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.
Ključne riječi
smart meter; display defects; YOLOv5; ResNet34
Hrčak ID:
265145
URI
Datum izdavanja:
21.10.2021.
Posjeta: 646 *