{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:20:10Z","timestamp":1780392010911,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T00:00:00Z","timestamp":1603238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automatic reading of pointer meters is of great significance for efficient measurement of industrial meters. However, existing algorithms are defective in the accuracy and robustness to illumination shooting angle when detecting various pointer meters. Hence, a novel algorithm for adaptive detection of different pointer meters was presented. Above all, deep learning was introduced to detect and recognize scale value text in the meter dial. Then, the image was rectified and meter center was determined based on text coordinate. Next, the circular arc scale region was transformed into a linear scale region by polar transform, and the horizontal positions of pointer and scale line were obtained based on secondary search in the expanded graph. Finally, the distance method was used to read the scale region where the pointer is located. Test results showed that the algorithm proposed in this paper has higher accuracy and robustness in detecting different types of meters.<\/jats:p>","DOI":"10.3390\/s20205946","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T20:51:00Z","timestamp":1603399860000},"page":"5946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6373-5846","authenticated-orcid":false,"given":"Zhu","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5919-7499","authenticated-orcid":false,"given":"Yisha","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1915-1960","authenticated-orcid":false,"given":"Qinghua","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3306-7335","authenticated-orcid":false,"given":"Kunjian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7369-7465","authenticated-orcid":false,"given":"Jian","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lai, H., Kang, Q., Pan, L., and Cui, C. (2019, January 25\u201328). A Novel Scale Recognition Method for Pointer Meters Adapted to Different Types and Shapes. Proceedings of the 2019 IEEE 15th International Conference on Automation Science and Engineering, Vancouver, BC, Canada.","DOI":"10.1109\/COASE.2019.8843107"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bao, H., Tan, Q., Liu, S., and Miao, J. (2019). Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping. Appl. Sci., 9.","DOI":"10.3390\/app9183729"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.4304\/jcp.8.5.1309-1314","article-title":"Automatic Value Identification of Pointer-Type Pressure Gauge Based on Machine Vision","volume":"8","author":"Ye","year":"2013","journal-title":"J. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"787","DOI":"10.4304\/jcp.9.4.787-793","article-title":"Auto-recognition Method for Pointer-type Meter Based on Binocular Vision","volume":"9","author":"Yang","year":"2014","journal-title":"J. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"248","DOI":"10.4028\/www.scientific.net\/AMM.615.248","article-title":"Research on the Image Enhancement Algorithm of Pointer Instrument under Inadequate Light","volume":"615","author":"Zhang","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_6","first-page":"203","article-title":"Flight Guardian: Autonomous Flight Safety Improvement by Monitoring Aircraft Cockpit Instruments","volume":"15","author":"Khan","year":"2018","journal-title":"J. Aerosp. Inf. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1515\/phys-2019-0010","article-title":"A pointer location algorithm for computer vision based automatic reading recognition of pointer gauges","volume":"17","author":"Tian","year":"2019","journal-title":"Open Phys."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Wang, J. (2018). Computer Vision-Based Approach for Reading Analog Multimeter. Appl. Sci., 8.","DOI":"10.3390\/app8081268"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wen, K., Li, D., Zhao, X., Fan, A., Mao, Y., and Zheng, S. (2018, January 12\u201314). Lightning Arrester Monitor Pointer Meter and Digits Reading Recognition Based on Image Processing. Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, Chongqing, China.","DOI":"10.1109\/IAEAC.2018.8577545"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sowah, R.A., Ofoli, A.R., Mensah-Ananoo, E., Mills, G.A., and Koumadi, K.M. (2018, January 23\u201327). Intelligent Instrument Reader Using Computer Vision and Machine Learning. Proceedings of the 2018 IEEE Industry Applications Society Annual Meeting, Portland, OR, USA.","DOI":"10.1109\/IAS.2018.8544601"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/19.836317","article-title":"Automatic calibration of analog and digital measuring instruments using computer vision","volume":"49","author":"Alegria","year":"2000","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.measurement.2012.06.005","article-title":"Segmentation-free approaches of computer vision for automatic calibration of digital and analog instruments","volume":"46","author":"Belan","year":"2013","journal-title":"Measurement"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.measurement.2016.06.045","article-title":"A robust and automatic recognition system of analog instruments in power system by using computer vision","volume":"92","author":"Zheng","year":"2016","journal-title":"Measurement"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5413","DOI":"10.1109\/TII.2019.2905662","article-title":"Character Segmentation-Based Coarse-Fine Approach for Automobile Dashboard Detection","volume":"15","author":"Gao","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"015401","DOI":"10.1088\/1361-6501\/aaed0a","article-title":"A robust and high-precision automatic reading algorithm of pointer meters based on machine vision","volume":"30","author":"Ma","year":"2019","journal-title":"Meas. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chi, J., Liu, L., Liu, J., Jiang, Z., and Zhang, G. (2015). Machine Vision Based Automatic Detection Method of Indicating Values of a Pointer Gauge. Math. Probl. Eng., 283629.","DOI":"10.1155\/2015\/283629"},{"key":"ref_17","first-page":"230","article-title":"Automatic reading method of pointer meter based on double Hough space voting","volume":"40","author":"Sheng","year":"2019","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107333","DOI":"10.1016\/j.measurement.2019.107333","article-title":"A detection and recognition system of pointer meters in substations based on computer vision","volume":"152","author":"Liu","year":"2020","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Dang, X., Lv, Q., and Liu, S. (2020, January 6\u20138). A Pointer Meter Recognition Algorithm Based on Deep Learning. Proceedings of the 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), Shenzhen, China.","DOI":"10.1109\/AEMCSE50948.2020.00068"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107962","DOI":"10.1016\/j.measurement.2020.107962","article-title":"A pointer meter recognition method based on virtual sample generation technology","volume":"163","author":"Cai","year":"2020","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, P., Zuo, L., Zhang, C., and Zhang, Z. (2019, January 2\u20135). A Value Recognition Algorithm for Pointer Meter Based on Improved Mask-RCNN. Proceedings of the 9th International Conference on Information Science and Technology (ICIST), Hulunbuir, China.","DOI":"10.1109\/ICIST.2019.8836852"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"311","DOI":"10.3166\/ejc.7.311-327","article-title":"Support Vector Machines: A Nonlinear Modelling and Control Perspective","volume":"7","author":"Suykens","year":"2001","journal-title":"Eur. J. Control"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., and Yan, J. (2018, January 18\u201322). FOTS: Fast Oriented Text Spotting with a Unified Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00595"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201312). Deep Residual Learning for Image Recognition. In Computer Vision and Pattern Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10851-005-0482-8","article-title":"Least Squares Fitting of Circles","volume":"23","author":"Chernov","year":"2005","journal-title":"J. Math. Imaging Vis."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gupta, A., Vedaldi, A., and Zisserman, A. (2016, January 27\u201330). Synthetic Data for Text Localisation in Natural Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.254"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5946\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:25:12Z","timestamp":1760178312000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/20\/5946"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,21]]},"references-count":26,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20205946"],"URL":"https:\/\/doi.org\/10.3390\/s20205946","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,21]]}}}