Another way: Direct regression of meter readings for circular pointer meter images

Pointer meters are widely used in various industries, and there is a growing demand for automatic and non-intrusive access to meter readings. The existing methods of automatically calculating meter readings involve complex processes which are time-consuming and have low automation performance. The paper presents the design of a Separate Dual-Selective Attention Mechanism (SDSAM) module and a Reading Correction Module (RCM). We also introduce the SDSM-DenseNet model, which utilizes dense connections to directly predict meter readings based on meter images. The SDSM-DenseNet model demonstrates high automation performance and reduces reliance on inherent characteristics of the meter. Compared to other attention mechanism models and feature extraction models, the SDSM-DenseNet model achieves lower error rates in calculating meter readings. Furthermore, when compared with representative methods for reading meters, the average error rate of the SDSM-DenseNet model is reduced by approximately 48%, while only requiring 0.021 s to predict a single image.