Continuous Monitoring of Water Levels for Industrial Boilers Using Single‐Stage Object Recognition YOLOv5
This paper presents a measurement method that utilizes object recognition technology for continuous and quantitative real‐time monitoring of water levels in industrial boilers. Real‐time videos of water levels were monitored using a small camera, and the YOLO algorithm, a single‐stage detector, was...
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| Veröffentlicht in: | International journal of energy research 2024-01, Vol.2024 (1) |
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| container_title | International journal of energy research |
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| creator | Kim, Jongwon Kwon, Minjun So, Byeongchan Kim, Sewon So, Hongyun |
| description | This paper presents a measurement method that utilizes object recognition technology for continuous and quantitative real‐time monitoring of water levels in industrial boilers. Real‐time videos of water levels were monitored using a small camera, and the YOLO algorithm, a single‐stage detector, was employed to use the bounding boxes of detected objects within the video as variables, directly measuring the length ratio for each frame. The method demonstrated a high level of accuracy in water‐level measurement, with an average of 99.02%, and a stable performance, with a fluctuation of 0.13% in continuous measurements. Consequently, the proposed measurement method proves feasible for quantifying continuous water levels in industrial inspection systems even in low‐resource environments. These results demonstrate a new mechanism for monitoring technology, without requiring text detection, showing the potential for improving efficiency in complex boiler systems and the feasibility of reliable water‐level measurement and control. |
| doi_str_mv | 10.1155/2024/6107765 |
| format | Article |
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Real‐time videos of water levels were monitored using a small camera, and the YOLO algorithm, a single‐stage detector, was employed to use the bounding boxes of detected objects within the video as variables, directly measuring the length ratio for each frame. The method demonstrated a high level of accuracy in water‐level measurement, with an average of 99.02%, and a stable performance, with a fluctuation of 0.13% in continuous measurements. Consequently, the proposed measurement method proves feasible for quantifying continuous water levels in industrial inspection systems even in low‐resource environments. These results demonstrate a new mechanism for monitoring technology, without requiring text detection, showing the potential for improving efficiency in complex boiler systems and the feasibility of reliable water‐level measurement and control.</description><identifier>ISSN: 0363-907X</identifier><identifier>EISSN: 1099-114X</identifier><identifier>DOI: 10.1155/2024/6107765</identifier><language>eng</language><publisher>Bognor Regis: Hindawi Limited</publisher><subject>Algorithms ; Boilers ; Efficiency ; Environmental monitoring ; Feasibility ; Gauges ; Inspection ; Measurement methods ; Measurement techniques ; Monitoring ; Object recognition ; Pattern recognition ; Real time ; Time measurement ; Water level fluctuations ; Water levels ; Water monitoring ; Water supply</subject><ispartof>International journal of energy research, 2024-01, Vol.2024 (1)</ispartof><rights>Copyright © 2024 Jongwon Kim et al. 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| subjects | Algorithms Boilers Efficiency Environmental monitoring Feasibility Gauges Inspection Measurement methods Measurement techniques Monitoring Object recognition Pattern recognition Real time Time measurement Water level fluctuations Water levels Water monitoring Water supply |
| title | Continuous Monitoring of Water Levels for Industrial Boilers Using Single‐Stage Object Recognition YOLOv5 |
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