Computer Vision Based Energy Monitoring System using Meter Image Capturing System (MICAPS)

Bajaj, Simran; S, Charan Teja; Yemula, Pradeep Kumar

doi:10.1109/icpc2t48082.2020.9071459

Cited by 3 publications

(1 citation statement)

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“…The final application shows that the automatic sorting system constructed by the research results has a simple interface and is easy to operate, which can greatly improve the efficiency and quality of electricity meter recycling sorting and has certain practical significance for the development of the national power grid industry. Bajaj and Yemula [24] proposed a Meter Image Capture and Processing System (MICAPS). MICAPS is an image-based data extraction technology.…”

Section: Introductionmentioning

confidence: 99%

Smart Energy Monitoring and Analysis Method Based on Image Recognition Technology

Yu¹,

Ali²,

Khan³

et al. 2023

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Smart energy monitoring and analysis based on image recognition technology can provide more accurate and real-time data support for energy systems, improving the efficiency and level of energy management. The method is sensitive to factors such as image quality, illumination, and angle, and when the image quality is not high, the recognition effect may be poor. Some methods, such as feature extraction and deep learning methods, have a large amount of computation and relatively poor real-time performance, which may affect the timeliness of energy monitoring. Therefore, this study conducts a study on smart energy monitoring and analysis methods based on image recognition technology. The energy monitoring instrument panel is preprocessed with brightness adjustment and Hough transform. After extracting the pointer instrument panel, the pointer detection and angle calculation are performed by using connected domain analysis, thinning algorithm, line fitting, and pointer direction judgment mechanism. The energy monitoring instrument reading recognition method is given. The effectiveness of the proposed method is verified through experimental results analysis.

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Section: Introductionmentioning

confidence: 99%