Automatic pointer meters recognition system based on line scan vision
The automatic recognition of pointers is of great significance for efficiently collecting measurements from industrial instruments. In this paper, an automatic pointer meter recognition system based on line scan vision is developed. A line scan camera is used to capture images of the pointer of a po...
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| Published in | Measurement science & technology Vol. 33; no. 12; p. 127001 |
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| Main Author | |
| Format | Journal Article |
| Language | English Japanese |
| Published |
01.12.2022
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| Online Access | Get full text |
| ISSN | 0957-0233 1361-6501 |
| DOI | 10.1088/1361-6501/ac8b9c |
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| Abstract | The automatic recognition of pointers is of great significance for efficiently collecting measurements from industrial instruments. In this paper, an automatic pointer meter recognition system based on line scan vision is developed. A line scan camera is used to capture images of the pointer of a pointer meter. Light-spot centroid algorithm is implemented to determine the centroid position of the pointer. The captured images can record the dynamic movement of the pointer, thereby enabling condition monitoring in industrial processes. Experimental results show that the proposed pointer meter extraction method is robust against interference and that a characteristic segmentation classifier produces more accurate detection results than other approaches. |
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| AbstractList | The automatic recognition of pointers is of great significance for efficiently collecting measurements from industrial instruments. In this paper, an automatic pointer meter recognition system based on line scan vision is developed. A line scan camera is used to capture images of the pointer of a pointer meter. Light-spot centroid algorithm is implemented to determine the centroid position of the pointer. The captured images can record the dynamic movement of the pointer, thereby enabling condition monitoring in industrial processes. Experimental results show that the proposed pointer meter extraction method is robust against interference and that a characteristic segmentation classifier produces more accurate detection results than other approaches. |
| Author | Wang, Qing |
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| Cites_doi | 10.1016/j.measurement.2020.107962 10.1109/19.836317 10.1088/0957-0233/20/6/065106 10.1016/j.measurement.2012.01.031 10.1364/AO.55.002393 10.1088/1361-6501/aaed0a 10.1016/j.optlastec.2011.03.029 10.1109/JSEN.2012.2231940 10.1049/iet-smt.2013.0054 10.1088/0957-0233/18/5/058 |
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| References | Alegria (mstac8b9cbib5) 2000; 49 Behaine (mstac8b9cbib6) 2014; 8 Vázquez-Fernández (mstac8b9cbib1) 2009; 20 Jaffery (mstac8b9cbib3) 2013; 13 Liu (mstac8b9cbib10) 2011; 43 Cai (mstac8b9cbib2) 2020; 163 Ma (mstac8b9cbib8) 2019; 30 Hemming (mstac8b9cbib7) 2007; 18 Aydogmus (mstac8b9cbib4) 2012; 45 Wang (mstac8b9cbib9) 2016; 55 |
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