Computer Vision-Based Approach for Reading Analog Multimeter
AbstractMultimeters are useful instruments for measuring electronic parameters. Even though the digital multimeter is commonly used in our daily life under the considerations of precision and cost, the analog multimeter is still preferable in many applications due to its easy use to monitor promptly varying values. However, the reading of analog multimeters (or A-meter) usually relies on human eyes with two obvious drawbacks of inefficiency and easy fatigue, while visual inspection onto an A-meter is needed for a long period of time. From the viewpoint of optical sensor application, computer vision, like human eyes, can also be used to sense stimuli from the real world. Therefore, in this paper, an approach of reading an A-meter based on a computer vision technique is proposed. Reading an A-meter relies on information from the arrow on the function selector and the pointer on the instrument meter; the presented method is thus mainly composed of horizontal alignment of the A-meter, detection of the instrument meter region, angle detection of the selector arrow, and angle detection of the pointer. In addition, the schemes of edge-based geometric matching (EGM) and pyramidal gradient matching (PGM) are adopted to detect the regions of interest. 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 finally read the A-meter. The often used scenarios for reading AC voltage, DC voltage, and DC current as well as resistance are used for experiments and evaluations. The experimental results show that the accuracy of detecting the function selected is 100%, the mean accuracy of reading a value from the A-meter is 95% or above, except for some cases of reading resistance that are affected by the so-called little-change-large-multiplier effect. The proposed method can perform very well as long as the mean intensity is
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Chen, Y.-S.; Wang, J.-Y. Computer Vision-Based Approach for Reading Analog Multimeter. Appl. Sci. 2018, 8, 1268.
Chen Y-S, Wang J-Y. Computer Vision-Based Approach for Reading Analog Multimeter. Applied Sciences. 2018; 8(8):1268.Chicago/Turabian Style
Chen, Yung-Sheng; Wang, Jeng-Yau. 2018. "Computer Vision-Based Approach for Reading Analog Multimeter." Appl. Sci. 8, no. 8: 1268.
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