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Real-time reading system for pointer meter based on YolactEdge

Yang, Chengjun (författare)
Zhengzhou Univ, Peoples R China; Hechi Univ, Peoples R China
Zhu, Ruijie (författare)
Zhengzhou Univ, Peoples R China
Yu, Xinde (författare)
Zhengzhou Univ, Peoples R China
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Yang, Ce (författare)
Hunan Univ Sci & Technol, Peoples R China
Xiao, Lijun (författare)
Hunan Univ Sci & Technol, Peoples R China
Fowler, Scott (författare)
Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten
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 (creator_code:org_t)
Taylor & Francis, 2023
2023
Engelska.
Ingår i: Connection science (Print). - : Taylor & Francis. - 0954-0091 .- 1360-0494. ; 35:1, s. 1-23
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

Real-time monitoring;deep-vision; edgecomputing; instancesegmentation; Jetson XavierNX

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