Computer Vision-Based Approach for Reading Analog Multimeter

Multimeter is a useful instrument for measuring electronic parameters. Even though 1 the digital multimeter is commonly used in our daily life under the considerations of precision 2 and cost, the analog multimeter is still a preferable in many applications due to its easy use to 3 monitor a promptly varying value. However, reading such an analog multimeter (or A-meter) usually 4 relies on human eyes with two obvious drawbacks of inefficiency and easy fatigue while visual 5 inspection onto an A-meter is needed for a long period of time. From the viewpoint of optical sensor 6 application, computer vision like human eyes can also be used to sense the stimuli from the real 7 world. Therefore, in this paper an approach of reading A-meter based on computer vision technique 8 is proposed. Reading an A-meter relies on the information of the arrow on the function selector and 9 the pointer on the instrument meter, the presented method is thus mainly composed of horizontal 10 alignment of A-meter, detection of instrument meter region, angle detection of selector arrow, and 11 angle detection of the pointer. In addition, the schemes of edge-based geometric matching (EGM) 12 and pyramidal gradient matching (PGM) are adopted for detecting the wanted regions of interest. 13 The mapping relationship between function selector and the selector arrow as well as that between 14 instrument meter and the pointer are built and formulated for finally reading the A-meter. The often 15 used scenarios of reading AC voltage, DC voltage, DC current as well as resistance are used for 16 experiments and evaluations. Experimental results show that the accuracy of detecting the function 17 select is 100%, the mean accuracy of reading value from the A-meter is 95% above except for some 18 cases of reading resistance affected by the so-called little-change-large-multiplier effect. The proposed 19 method can perform very well as long as the mean intensity≥ 7.5. Based on a suitable modification of 20 the proposed method, an application of monitoring storage level meter and pressure meter installed 21 on a 15m3 Liquid Nitrogen (LN2) tank is demonstrated. Our experiments and demonstrations 22 confirm the feasibility of the proposed approach. 23

[1]  Walter G. Kropatsch,et al.  Automatic reading of analog display instruments , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[2]  Zhang Yanling,et al.  A New Method of Automatic Reading of High-precision Pointer Meter , 2006, 2007 Chinese Control Conference.

[3]  Wasiq Khan,et al.  Automated aircraft instrument reading using real time video analysis , 2016, 2016 IEEE 8th International Conference on Intelligent Systems (IS).

[4]  Danilo Alves de Lima,et al.  A COMPUTER VISION SYSTEM TO READ METER DISPLAYS , 2008 .

[5]  Xiaofen Xing,et al.  Blurred target tracking based on sparse representation of online updated templates , 2016, 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP).

[6]  Juping Gu,et al.  A Template Update Cam Shift Algorithm Based on LTP Texture , 2015, 2015 International Conference on Computer Science and Mechanical Automation (CSMA).

[7]  Rong Hui,et al.  Detection system of meter pointer based on computer vision , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[8]  Peng Ren,et al.  Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates , 2016 .

[9]  Zhang Hong,et al.  Pointer-Type Meter Reading Method Research Based on Image Processing Technology , 2010, 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing.

[10]  Yung-Sheng Chen,et al.  Computer vision on color-band resistor and its cost-effective diffuse light source design , 2016, J. Electronic Imaging.

[11]  W.L. Chan,et al.  Computer vision application in automatic meter calibration , 2005, Fourtieth IAS Annual Meeting. Conference Record of the 2005 Industry Applications Conference, 2005..

[12]  Wasiq Khan,et al.  Flight Guardian: Autonomous Flight Safety Improvement by Monitoring Aircraft Cockpit Instruments , 2018 .

[13]  Yuan Li,et al.  Research on the Pre-processing Method of Automatic Reading Water Meter System , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.