View in EDS

Decoding Dot Peen Data Matrix Code with Deep Learning Capability for Product Traceability

Saved in:
Bibliographic Details
Title: Decoding Dot Peen Data Matrix Code with Deep Learning Capability for Product Traceability
Authors: Loh, Siu Hong, Teh, Peh Chiong, Sim, Jia Jia, Tai, Chian Kwang, Yeap, Kim Ho, Lee, Yu Jen, Mazlan, Ahmad Uzair
Source: Applications of Modelling and Simulation; Vol 7 (2023); 38-48 ; 2600-8084
Publisher Information: ARQII Publication
Publication Year: 2023
Subject Terms: Data matrix code, Deep learning, Direct part marking, Dot peen marking
Description: An approach for recognizing and decoding the industrial-based dot peen data matrix code is presented in this paper. Dot peen marking is a type of direct part marking (DPM). Due to the reduced contrast characteristic, it could be difficult to read a DPM code. Additionally, the readability of a DPM code may deteriorate over time due to partial degradation on the product surface. A deep-learning-based method using You-Only-Look-Once (YOLO) v5 model is proposed. Firstly, a large dataset of dot peen data matrix symbols was prepared to initiate the YOLOv5 model training. Image data augmentation was then applied to the training images to increase the size of the training dataset. The YOLOv5 model training was processed with a batch size of 16 and the epochs number of 60 due to its high accuracy (97.79%). All dot peen data matrix codes were detected accurately within one second, fulfilling our intention to design a high-speed reader for industrial-based dot peen data matrix. With ANOVA analysis, we observed that the brightness level and the camera distance significantly affect the decoding process. Additionally, our developed model can successfully decode a partially damage code if the level of damage is below 30%.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/385/149; http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/385
Availability: http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/385
Rights: Copyright (c) 2023 Siu Hong Loh, Peh Chiong Teh, Jia Jia Sim, Chian Kwang Tai, Kim Ho Yeap, Yu Jen Lee, Ahmad Uzair Mazlan ; http://creativecommons.org/licenses/by/4.0
Accession Number: edsbas.692F3B46
Database: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/385#
    Name: EDS - BASE (s4221598)
    Category: fullText
    Text: View record from BASE
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Loh%20SH
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsbas
DbLabel: BASE
An: edsbas.692F3B46
RelevancyScore: 944
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 943.653564453125
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Decoding Dot Peen Data Matrix Code with Deep Learning Capability for Product Traceability
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Loh%2C+Siu+Hong%22">Loh, Siu Hong</searchLink><br /><searchLink fieldCode="AR" term="%22Teh%2C+Peh+Chiong%22">Teh, Peh Chiong</searchLink><br /><searchLink fieldCode="AR" term="%22Sim%2C+Jia+Jia%22">Sim, Jia Jia</searchLink><br /><searchLink fieldCode="AR" term="%22Tai%2C+Chian+Kwang%22">Tai, Chian Kwang</searchLink><br /><searchLink fieldCode="AR" term="%22Yeap%2C+Kim+Ho%22">Yeap, Kim Ho</searchLink><br /><searchLink fieldCode="AR" term="%22Lee%2C+Yu+Jen%22">Lee, Yu Jen</searchLink><br /><searchLink fieldCode="AR" term="%22Mazlan%2C+Ahmad+Uzair%22">Mazlan, Ahmad Uzair</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Applications of Modelling and Simulation; Vol 7 (2023); 38-48 ; 2600-8084
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: ARQII Publication
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Data+matrix+code%22">Data matrix code</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Direct+part+marking%22">Direct part marking</searchLink><br /><searchLink fieldCode="DE" term="%22Dot+peen+marking%22">Dot peen marking</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: An approach for recognizing and decoding the industrial-based dot peen data matrix code is presented in this paper. Dot peen marking is a type of direct part marking (DPM). Due to the reduced contrast characteristic, it could be difficult to read a DPM code. Additionally, the readability of a DPM code may deteriorate over time due to partial degradation on the product surface. A deep-learning-based method using You-Only-Look-Once (YOLO) v5 model is proposed. Firstly, a large dataset of dot peen data matrix symbols was prepared to initiate the YOLOv5 model training. Image data augmentation was then applied to the training images to increase the size of the training dataset. The YOLOv5 model training was processed with a batch size of 16 and the epochs number of 60 due to its high accuracy (97.79%). All dot peen data matrix codes were detected accurately within one second, fulfilling our intention to design a high-speed reader for industrial-based dot peen data matrix. With ANOVA analysis, we observed that the brightness level and the camera distance significantly affect the decoding process. Additionally, our developed model can successfully decode a partially damage code if the level of damage is below 30%.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: application/pdf
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/385/149; http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/385
– Name: URL
  Label: Availability
  Group: URL
  Data: http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/385
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: Copyright (c) 2023 Siu Hong Loh, Peh Chiong Teh, Jia Jia Sim, Chian Kwang Tai, Kim Ho Yeap, Yu Jen Lee, Ahmad Uzair Mazlan ; http://creativecommons.org/licenses/by/4.0
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.692F3B46
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.692F3B46
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Data matrix code
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Direct part marking
        Type: general
      – SubjectFull: Dot peen marking
        Type: general
    Titles:
      – TitleFull: Decoding Dot Peen Data Matrix Code with Deep Learning Capability for Product Traceability
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Loh, Siu Hong
      – PersonEntity:
          Name:
            NameFull: Teh, Peh Chiong
      – PersonEntity:
          Name:
            NameFull: Sim, Jia Jia
      – PersonEntity:
          Name:
            NameFull: Tai, Chian Kwang
      – PersonEntity:
          Name:
            NameFull: Yeap, Kim Ho
      – PersonEntity:
          Name:
            NameFull: Lee, Yu Jen
      – PersonEntity:
          Name:
            NameFull: Mazlan, Ahmad Uzair
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
          Titles:
            – TitleFull: Applications of Modelling and Simulation; Vol 7 (2023
              Type: main
ResultId 1