Acceso abierto

Least-squares method and deep learning in the identification and analysis of name-plates of power equipment

Yerong Zhong

Yerong Zhong

Qingyuan Power Supply Bureau of Guangdong Power Grid Co., LtdQingyuan, China
Buscar este autor en
Sciendo|Google Scholar
Zhong, Yerong
, 
Guoheng Ruan

Guoheng Ruan

Qingyuan Power Supply Bureau of Guangdong Power Grid Co., LtdQingyuan, China
Buscar este autor en
Sciendo|Google Scholar
Ruan, Guoheng
, 
Ehab Abozinadah

Ehab Abozinadah

Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz UniversityJeddah, Saudi Arabia
Buscar este autor en
Sciendo|Google Scholar
Abozinadah, Ehab
 y 
Jiaming Jiang

Jiaming Jiang

Qingyuan Power Supply Bureau of Guangdong Power Grid Co., LtdQingyuan, China
Buscar este autor en
Sciendo|Google Scholar
Jiang, Jiaming
  
15 dic 2021
Least-squares method and deep learning in the identification and analysis of name-plates of power equipment's Cover Image
Acerca de este artículo
Artículo anterior
Artículo siguiente
Cite
Descargar portada
Publicado en línea: 15 dic 2021
Páginas: 103 - 112
Recibido: 16 jun 2021
Aceptado: 24 sept 2021
DOI: https://doi.org/10.2478/amns.2021.1.00055
Palabras clave
© 2021 Yerong Zhong et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

The overall architecture of the model.
The overall architecture of the model.

Fig. 2

Text recognition network.
Text recognition network.

Fig. 3

The loss curve of the text detection network.
The loss curve of the text detection network.

Fig. 4

The overall loss curve of the model.
The overall loss curve of the model.

Fig. 5

SVT partial text detection results.
SVT partial text detection results.

Fig. 6

Example of nameplate recognition results.
Example of nameplate recognition results.

Comparison of experimental results based on the SVT dataset

Method F-value, %
Sundararajan et al. [1] 53.00
Yang et al. [2] 64.00
O’Brien et al. [3] 66.18
CTPN + CRNN 64.34
CTPN + OCR software 42.50
This study 68.69

Comparison of nameplate recognition accuracy

Method OCR software Sundararajan et al. [1] CTPN + CRNN Yang et al. [2] This study
Accuracy, % 67.1 82.7 82.34 84.11 87.71

Partial image processing time

Size Number of characters/piece Detection time, s Recognition time, s Total time, s
301 × 472 31 0.341 2.76 3.101
300 × 401 70 0.289 2.1 2.389
450 × 800 83 0.364 2.25 2.614
532 × 710 73 0.141 2.81 2.951
241 × 629 78 0.321 2.66 2.981
3024 × 4032 63 0.542 2.12 2.662
Idioma:
Inglés
Calendario de la edición:
1 veces al año
Temas de la revista:
Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros
RSS Feed de revista