# Computer Vision-Based Approach for Reading Analog Multimeter
## 7 Conclusions and Future Work  
The analog multimeter (A-meter) is a significant instrument for sensing or measuring electronic signals, e.g., voltage, current, resistance, and so on. Even though the digital multimeter is commonly used in our daily life under the considerations of precision and cost, the analog multimeter is still a preferable device for many applications due to its ability to monitor promptly varying values. In order to retain the merits of A-meter and to reduce the tiredness of human eyes observing the A-meter over long durations, it is of great importance to develop a computer vision-based method to automatically read the measured value indicated by the pointer of the A-meter. To effectively read an A-meter, the presented algorithms include horizontal alignment of the A-meter, detection of the instrument meter region, angle detection of the selector arrow, and angle detection of the pointer. The desired ROIs used in our algorithms are extracted by two template matching methods--edge-based geometric matching (**EGM**) and pyramidal gradient matching (**PGM**)--which have been widely used and embedded in the NI vision software for machine vision applications [21] and for LabVIEW [19]. Moreover, the mapping relationship between the function selector and the selector arrow, as well as that between the instrument meter and the pointer are built and formulated to read the A-meter, where the scale on the meter may be linear or nonlinear. The often used scenarios of reading AC voltage, DC voltage, and DC current as well as resistance were adopted for the experiments and evaluations in this study. Our experimental results showed that using our method, the accuracy of detecting the function select was 100%, and the mean accuracy of reading values from the A-meter was 95% or above, except for some cases of reading resistance affected by the so-called _little-change-large-multiplier effect_. In addition, the proposed method performed very well as long as the mean intensity, i.e., illumination was \(\geq\) 7.5. The \"uncertainty problem\" in reading an analog meter was explored using translation and/or rotation of the camera to read the A-meter by the proposed computer vision-based approach. The effective range to use our method within was also investigated and validated. Based on suitable modification of the proposed method, an application involving the monitoring of a storage level meter and pressure meter installed on a 15 m\({}^{3}\) liquid nitrogen (LN2) tank was demonstrated successfully. In conclusion, even if the A-meter is rotated, slightly tilted and/or illumination varied, the proposed computer vision-based approach can effectively read the analog multimeter. A general purpose method for reading the A-meter, its real-time implementation and a time complexity analysis, as well as a more deep investigation on the uncertainty issue are worthy of further study and could be regarded as our future works.  
Conceptualization, Y.-S.C.; Data curation, J.-Y.W.; Formal analysis, Y.-S.C. and J.-Y.W.; Investigation, Y.-S.C. and J.-Y.W.; Methodology, Y.-S.C. and J.-Y.W.; Software, J.-Y.W.; Writing--original draft, Y.-[PERSON] and [PERSON].  
This research received no external funding.  
This work was supported in part by the National Chung-Shan Institute of Science and Technology, Taiwan, Republic of China. The IP camera and relative materials were purchased under the number of SXJ0399013.  
The authors declare no conflict of interest.