Abstract
As an instrument widely used in substations, pointer meters are mainly used to detect the working status of substation equipment, so regular calibration is very important. In order to solve the problems of extraction difficulty, large positioning error and poor recognition accuracy in the reading recognition of pointer meters, a method for automatic reading detection and recognition of pointer meters based on deep learning is proposed. Use the improved EAST algorithm to extract character features, and perform module detection on the results. It is concluded that the automatic detection and recognition of analog meters under complex backgrounds has good accuracy and stability, which can meet the application needs of substations.
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