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Analysis of institutional authors

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June 13, 2025
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A novel automatic reading method of pointer meters based on deep learning

Publicated to: Neural Computing & Applications. 35 (11): 8357-8370 - 2023-01-01 35(11), DOI: 10.1007/s00521-022-08110-7

Authors:

Sun J; Huang Z; Zhang Y
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Affiliations

Faculty of Information Technology; Beijing University of Technology; Beijing; 100124; China - Author
School of Electronic and Information Engineering; Beijing Jiaotong University; Beijing; 100044; China - Author

Abstract

The automatic reading of pointer meters is significantly important to data monitoring and efficient measurement in the industrial field. However, the existing automatic reading method can not obtain accurate performance in natural scenarios and present no satisfactory application effects in industrial fields (such as power stations and gas stations). In this paper, a novel automatic reading method for pointer meters based on deep learning is proposed, which contains five stages. Stage-1: the object detection algorithm Yolov4 and the feature optimization module IFF are used to locate the target meter. Stage-2: Semantic segmentation model is applied to extract the pointer area based on Anam-Net. Stage-3: the character detection algorithm CRAFT and the text recognition algorithm E2E-MLT are combined and used to recognize the scale text and unit on the meter. Stage-4: the scale area of the meter is converted to the polar coordinate system, and a lightweight convolutional neural network is designed to locate the main scale line. And finally in Stage-5: the reading data are calculated according to the outputs of the above-mentioned deep learning models. The experiment results show that the reading method proposed in this paper has higher accuracy and robustness than those of the existing approaches and obtains satisfactory application effects in the industrial field. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
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Keywords

Application effectAutomatic pointer meter readingAutomatic readingCharacter recognitionComputer visionComputer vision (cv)ConvolutionConvolutional neural networkConvolutional neural network (cnn)Convolutional neural networksDeep learningDeep learning (dl)Industrial fieldsLearning systemsMeter readingsNatural sceneNatural scenesObject detectionSemanticsSignal detection

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Neural Computing & Applications due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category .

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.58, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-12-21, the following number of citations:

  • Scopus: 16
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-12-21:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 6.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 6 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (SUN, JUNJIAO) .

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