Application of Color Filtering and Image Filtering Algorithms in Circuit Diagnosis of Smart Meter Box

Chen, Ming; Ji, Chengyue; Mao, Dilin; Wang, Yu-nan; Ma, Yuan; Cao, Xiu

doi:10.1145/3545822.3545823

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…The energy industry also needs to keep pace with the times and use image recognition and other technological means for smart energy monitoring and analysis. In the various links of energy production, transportation, and use, it is necessary to monitor the operation of equipment and potential safety hazards in real time [14][15][16][17][18][19][20][21]. Image recognition technology can be used to quickly and accurately identify the abnormal conditions of energy equipment, thereby improving the operating efficiency and safety of energy equipment.…”

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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