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Development of image recognition system for analogue meter’s reading detection

Digitization is one of the major components in Industrial Revolution 4 (IR 4.0). It provides a lot of benefits to the industrial, such as increasing productivity, better data visualisation, simplifying parameters control, etc. Although many meters nowadays come with the smart Internet of Things (IoT...

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Main Author: Chong, Yue Jiet
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utar.edu.my/4963/1/3E_1702386_Final_report_%2D_YUE_JIET_CHONG.pdf
http://eprints.utar.edu.my/4963/
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spelling my-utar-eprints.49632022-12-23T13:22:04Z Development of image recognition system for analogue meter’s reading detection Chong, Yue Jiet TK Electrical engineering. Electronics Nuclear engineering Digitization is one of the major components in Industrial Revolution 4 (IR 4.0). It provides a lot of benefits to the industrial, such as increasing productivity, better data visualisation, simplifying parameters control, etc. Although many meters nowadays come with the smart Internet of Things (IoT) features, which provides real-time monitoring and data storing, many industries still prefer to continue using the existing analogue meters in their manufacturing plants as replacing the existing analogue meters with the cloud-connected digital meters can be very costly especially for industrial grade meters. In this project, a costeffective image recognition system to capture and digitize the analogue meter’s readings using deep learning model (SSD MobileNet) as well as optical character recognition (Tesseract) was demonstrated. The deep learning model has been trained with a dataset of 750 images and was used to detect the region of interest (meter’s readings). The OCR is used to convert the readings to string datatype. Besides, the image processing techniques via OpenCV library has been implemented for enhancing the quality of the ROI. The programme developed has been transferred and executed on the Raspberry Pi microcomputer with camera module attached to an analogue water meter. The results show that the accuracies of the deep learning model and OCR are 95% and 91%, respectively. In addition, the memory occupation of the deep learning model is about 10 MB, which is suited the embedded system with limited memory capacity. 2022 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4963/1/3E_1702386_Final_report_%2D_YUE_JIET_CHONG.pdf Chong, Yue Jiet (2022) Development of image recognition system for analogue meter’s reading detection. Final Year Project, UTAR. http://eprints.utar.edu.my/4963/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Chong, Yue Jiet
Development of image recognition system for analogue meter’s reading detection
description Digitization is one of the major components in Industrial Revolution 4 (IR 4.0). It provides a lot of benefits to the industrial, such as increasing productivity, better data visualisation, simplifying parameters control, etc. Although many meters nowadays come with the smart Internet of Things (IoT) features, which provides real-time monitoring and data storing, many industries still prefer to continue using the existing analogue meters in their manufacturing plants as replacing the existing analogue meters with the cloud-connected digital meters can be very costly especially for industrial grade meters. In this project, a costeffective image recognition system to capture and digitize the analogue meter’s readings using deep learning model (SSD MobileNet) as well as optical character recognition (Tesseract) was demonstrated. The deep learning model has been trained with a dataset of 750 images and was used to detect the region of interest (meter’s readings). The OCR is used to convert the readings to string datatype. Besides, the image processing techniques via OpenCV library has been implemented for enhancing the quality of the ROI. The programme developed has been transferred and executed on the Raspberry Pi microcomputer with camera module attached to an analogue water meter. The results show that the accuracies of the deep learning model and OCR are 95% and 91%, respectively. In addition, the memory occupation of the deep learning model is about 10 MB, which is suited the embedded system with limited memory capacity.
format Final Year Project / Dissertation / Thesis
author Chong, Yue Jiet
author_facet Chong, Yue Jiet
author_sort Chong, Yue Jiet
title Development of image recognition system for analogue meter’s reading detection
title_short Development of image recognition system for analogue meter’s reading detection
title_full Development of image recognition system for analogue meter’s reading detection
title_fullStr Development of image recognition system for analogue meter’s reading detection
title_full_unstemmed Development of image recognition system for analogue meter’s reading detection
title_sort development of image recognition system for analogue meter’s reading detection
publishDate 2022
url http://eprints.utar.edu.my/4963/1/3E_1702386_Final_report_%2D_YUE_JIET_CHONG.pdf
http://eprints.utar.edu.my/4963/
_version_ 1753792998526681088
score 13.211869

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