A Robust Pointer Meter Reading Recognition Method Based on TransUNet and Perspective Transformation Correction

The automatic reading recognition of pointer meters plays a crucial role in data monitoring and analysis in intelligent substations. Existing meter reading methods struggle to address challenging difficulties such as image distortion and varying illumination. To enhance their robustness and accuracy, this study proposes a novel approach that leverages the TransUNet semantic segmentation model and a perspective transformation correction method. Initially, the dial of the pointer meter is localized from the natural background using YOLOv8. Subsequently, after enhancing the image with Gamma correction technology, the scale lines and the pointer within the dial are extracted using the TransUNet model. The distorted or rotated dial can then be corrected through perspective transformation. Finally, the meter readings are accurately obtained by the Weighted Angle Method (WAM). Ablative and comparative experiments on two self-collected datasets clearly verify the effectiveness of the proposed method, with a reading accuracy of 97.81% on Simple-MeterData and 93.39% on Complex-MeterData, respectively.