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NVIDIA TX2-Based Inkjet Character Detection Algorithm.

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Τίτλος: NVIDIA TX2-Based Inkjet Character Detection Algorithm. (English)
Συγγραφείς: LI Fan, HU Weiping, LIU Beibei, LIU Yuge
Πηγή: Journal of Computer Engineering & Applications; Jul2022, Vol. 58 Issue 13, p210-216, 7p
Θεματικοί όροι: Text recognition, Pattern recognition systems, Algorithms, Food packaging, Food processing plants
Περίληψη: Aiming at the phenomenon of spray leakage, re-spray, deficiency of inkjet characters under the background of complex commodities, an inkjet character detection algorithm based on YOLOv5 + CRNN is proposed. The inkjet character positioning algorithm is based on YOLOv5, combined with the attention mechanism to improve its detection accuracy. Then, the number and complexity of model parameters are reduced through sparse training and channel pruning, so that the final detection accuracy is increased by 3.4 percentage points, and the amount of model parameters is reduced by 6.7 MB. After erasing background and perspective transformation, the positioned character area is send to the CRNN network to complete the recognition of inkjet character. Finally, the improved algorithm is deployed to the NVIDIA TX2 embedded platform. Through the actual measurement in the production lines of the food packaging factory, the testing speed of the model reaches 28 frame/s, the accuracy of character-localization is 99.4%, and the recognition rate is 95% with good robustness. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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DbLabel: Complementary Index
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PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: NVIDIA TX2-Based Inkjet Character Detection Algorithm. (English)
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22LI+Fan%22">LI Fan</searchLink><br /><searchLink fieldCode="AR" term="%22HU+Weiping%22">HU Weiping</searchLink><br /><searchLink fieldCode="AR" term="%22LIU+Beibei%22">LIU Beibei</searchLink><br /><searchLink fieldCode="AR" term="%22LIU+Yuge%22">LIU Yuge</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Journal of Computer Engineering & Applications; Jul2022, Vol. 58 Issue 13, p210-216, 7p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Text+recognition%22">Text recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Food+packaging%22">Food packaging</searchLink><br /><searchLink fieldCode="DE" term="%22Food+processing+plants%22">Food processing plants</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Aiming at the phenomenon of spray leakage, re-spray, deficiency of inkjet characters under the background of complex commodities, an inkjet character detection algorithm based on YOLOv5 + CRNN is proposed. The inkjet character positioning algorithm is based on YOLOv5, combined with the attention mechanism to improve its detection accuracy. Then, the number and complexity of model parameters are reduced through sparse training and channel pruning, so that the final detection accuracy is increased by 3.4 percentage points, and the amount of model parameters is reduced by 6.7 MB. After erasing background and perspective transformation, the positioned character area is send to the CRNN network to complete the recognition of inkjet character. Finally, the improved algorithm is deployed to the NVIDIA TX2 embedded platform. Through the actual measurement in the production lines of the food packaging factory, the testing speed of the model reaches 28 frame/s, the accuracy of character-localization is 99.4%, and the recognition rate is 95% with good robustness. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3778/j.issn.1002-8331.2107-0317
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      – Code: chi
        Text: Chinese
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      Pagination:
        PageCount: 7
        StartPage: 210
    Subjects:
      – SubjectFull: Text recognition
        Type: general
      – SubjectFull: Pattern recognition systems
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Food packaging
        Type: general
      – SubjectFull: Food processing plants
        Type: general
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      – TitleFull: NVIDIA TX2-Based Inkjet Character Detection Algorithm.
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            NameFull: HU Weiping
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            NameFull: LIU Beibei
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            – D: 01
              M: 07
              Text: Jul2022
              Type: published
              Y: 2022
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              Value: 58
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