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 multi scale feature fusion network (MSFFNet) scene fast text recognition (SFTR), while the proposed MSFFNet extracts pointer and scale line features. The multidirectional strip convolution (MDSConv) module in MSFFNet can extract strip features in different directions, and the multiscale 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 nonlinear 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.