To address the inefficiency, error⁃proneness, and safety risks associated with traditional manual meter reading for pressure instruments, as well as the limited adaptability of automated meter⁃reading technologies based on sensors and 3D vision, this study integrates computer vision and artificial intelligence technologies to develop a metering system that combines data acquisition, real⁃time monitoring, and data analysis. By improving the fast region⁃convolutional neural network（Fast R⁃CNN) algorithm through data augmentation and a lightweight feature extraction network, the system optimizes instrument positioning accuracy in complex environments. Additionally, the DeepLabv3+ model is enhanced by incorporating channel attention and spatial attention mechanisms, along with a hybrid loss function, to improve character segmentation efficiency. Experimental results demonstrate that the improved algorithm achieves an average positioning accuracy of 84% for instrument dial positioning and a mean Intersection over union of 78.6% for character segmentation in challenging industrial environments. Furthermore, the system reduces the time required for a single measurement by 85% compared to manual reading, confirming its high efficiency and strong adaptability. This research provides a scalable technical framework for intelligent monitoring of industrial equipment, offering the practical value for advancing digital and intelligent metering.