孙蓉蓉,王程斯,罗育林,田松林,庄秋乃,夏成文.基于深度学习和增强现实的智能变电站仪表读数识别研究[J].电测与仪表,2026,63(5):184-192. SUN Rongrong,WANG Chengsi,LUO Yulin,TIAN Songlin,ZHUANG Qiunai,XIA Chengwen.Research on intelligent substation instrument reading recognition based on deep learning and augmented reality[J].Electrical Measurement & Instrumentation,2026,63(5):184-192.
基于深度学习和增强现实的智能变电站仪表读数识别研究
Research on intelligent substation instrument reading recognition based on deep learning and augmented reality
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, an intelligent substation instrument reading recognition method based on improved deep learning method is proposed. The YOLOv8 model is improved to complete instrument classification and regional positioning, the DeepLabV3+model is improved to complete reading and recognition of pointer instruments, and the Transformer model is improved to complete digital instrument reading recognition, which verify its performance through experiments. The results indicate that, the 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%.
