Addressing the issues of poor recognition performance and the ability to only recognize pointer or digital instruments in existing intelligent substation instrument reading recognition methods

based on the augmented reality power inspection system

a smart substation instrument reading recognition method combining improved YOLOv8 model

improved DeepLabV3+model

and improved Transformer model has been proposed. Improve the YOLOv8 model to complete instrument classification and regional positioning

improve the DeepLabV3+model to complete reading and recognition of pointer instruments

and improve the Transformer model to complete digital instrument reading recognition

verify its performance through experiments. The results indicate that

improved YOLOv8 model effectively improves detection accuracy in instrument classification and positioning

with a detection accuracy rate greater than 98.00%. The improved DeepLabV3+model effectively improves segmentation accuracy in pointer instrument reading recognition

with a recognition error of less than 1.50%. The improved Transformer model effectively improves the recognition accuracy in digital instrument reading recognition

with a recognition accuracy rate greater than 97.00%. which can provide certain assistance for the safe operation of the power grid.