Currently,the oil-surface thermometer remains a crucial monitoring device in substations.However,challenges such as uneven scale distribution,inadequate model accuracy,and low reading precision persist in handling rea...Currently,the oil-surface thermometer remains a crucial monitoring device in substations.However,challenges such as uneven scale distribution,inadequate model accuracy,and low reading precision persist in handling readings from various types of oilsurface thermometers.This paper presents a high-precision recognition method based on neural networks for accurately reading various types of oil-surface thermometers.The proposed recognition method mainly comprises the YOLOv5 convolutional network,the attention U2-Net semantic segmentation network,and enhanced computational methods.This detection method demonstrates ultra-high precision and can be applied to different types of oilsurface thermometers.Compared to existing methods,the proposed detection method exhibits outstanding accuracy and effectiveness.A comprehensive set of tests was performed to assess the viability and reliability of the approach.Findings reveal that the maximum error margin in its measurements remains below 0.13%.展开更多
基金funded by the Zhejiang Provincial Administration for Market Regulation's Early Career Development Program,grant number CY2023323.
文摘Currently,the oil-surface thermometer remains a crucial monitoring device in substations.However,challenges such as uneven scale distribution,inadequate model accuracy,and low reading precision persist in handling readings from various types of oilsurface thermometers.This paper presents a high-precision recognition method based on neural networks for accurately reading various types of oil-surface thermometers.The proposed recognition method mainly comprises the YOLOv5 convolutional network,the attention U2-Net semantic segmentation network,and enhanced computational methods.This detection method demonstrates ultra-high precision and can be applied to different types of oilsurface thermometers.Compared to existing methods,the proposed detection method exhibits outstanding accuracy and effectiveness.A comprehensive set of tests was performed to assess the viability and reliability of the approach.Findings reveal that the maximum error margin in its measurements remains below 0.13%.
