A Fast Pointer Meter Reading Method Based on SFTR and MSFFNet

To enable rapid automatic reading of various pointer-type meters in different environments, this study designs a universal multitask cascaded network, divided into three stages: meter detection, feature extraction, and reading. First, pointer-type meters are detected using YOLOv12. Then, the text features of pointer-type meters are extracted using the designed SFTR, while the proposed MSFFNet extracts pointer and scale line features. The Multi-directional Strip convolution(MDSConv) module in MSFFNet can extract strip features in different directions, and the Multi-scale fusion(MSF) can efficiently perform feature fusion. Finally, the reading is calculated using the designed Difference calibration (Dc) reading method, which, through a segmented angle compensation mechanism combined with a non-linear interpolation strategy, can adaptively correct errors caused by uneven angles. Experimental results show that the proposed method has an average relative error of 0.38%, which is approximately 0.26 lower than existing advanced methods.