Abstract:This study proposes a method for pointer instrument reading recognition based on YOLOv8 and an improved UNet++ to solve the problem of low reading recognition accuracy caused by complex backgrounds and multiple rotational angles in images of substation meters. YOLOv8 is utilized to detect the instrument area, and perspective transformation is used for rotation correction. The improved UNet++, enhanced by a polarized self-attention module, is utilized to segment dial images to extract scales and pointer regions. After the pointer line is extracted, the instrument reading is computed using the angle method. Experimental results indicate that the proposed method achieves an average citation error of 1.82% in identifying instrument readings. The method has superior recognition accuracy and is feasible for application in the intelligent inspection of pointer instruments in substations.