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.