| The traditional manual meter reading for on-site measurement of pressure instruments is inefficient, error-prone, and poses safety risks. Automated meter- reading technologies based on sensors and 3D Vision have limitations. However, the OCR-based metering system, with the help of computer vision and artificial intelligence technologies, has become an efficient and accurate metering solution in complex industrial scenarios due to its advantages of no need for meter hardware modification and low cost. This study focuses on the application of OCR-based on-site metering techniques to pressure gauge scenarios, exploring methods for automated data collection, real-time monitoring, and data analysis. It examines the challenges faced by on-site equipment metering and the advantages of OCR technology. Building on these findings, the research improves upon gauge localization and character segmentation algorithms and designs a comprehensive metering system architecture that integrates data collection, processing, storage, and analysis functions. Subsequent experimental results effectively demonstrate the algorithm's efficacy and reliability, underscoring the significant potential of this technology to enhance the efficiency and accuracy of on-site equipment metering. |
