Computer vision‐based automated defect detection in ceramic bricks.

Nowadays, the development of cost‐effective, data‐driven technological processes using telecommunication technologies is essential. One of the focuses is on automating the process of evaluating the manufactured goods' quality. Vision‐based technology is now becoming increasingly used for monitoring...

Descripción completa

Detalles Bibliográficos
Publicado en:Systems Research & Behavioral Science Vol. 42; no. 4; pp. 1131 - 1142
Autores principales: Kataev, M. Y., Bulysheva, L. A.
Formato: Artículo
Publicado: Wiley-Blackwell Jul/Aug2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187948684&site=ehost-live
header:
  @attributes:
    shortDbName: ssf
    uiTerm: 187948684
    longDbName: Social Sciences Full Text (H.W. Wilson)
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        10927026
        2SN
      jtl: Systems Research & Behavioral Science
      issn: 10927026
      maglogo: Y
    pubinfo:
      dt: Jul/Aug2025
      vid: 42
      iid: 4
      pid: 480
      pub: Wiley-Blackwell
    artinfo:
      ui:
        187948684
        10.1002/sres.3040
      ppf: 1131
      ppct: 11
      formats:
      tig:
        atl: Computer vision‐based automated defect detection in ceramic bricks.
      aug:
        au:
          Kataev, M. Y.
          Bulysheva, L. A.
        affil:
          Department of Control Systems, Tomsk State University of Control Systems and Electronics, Tomsk, Russia
          Department of Information Technology and Decision Science, Old Dominion University, Norfolk Virginia,, USA
      su:
        Cost benefit analysis
        Information science
        Telecommunication
        Materials testing
        Computer software
        Mathematics
        Computer-assisted image analysis (Medicine)
        Quality control
        Descriptive statistics
        Artificial neural networks
        Digital image processing
        Automation
        Construction industry
        Algorithms
      sug:
        subj:
          Cost benefit analysis
          Information science
          Telecommunication
          Computer and Computer Peripheral Equipment and Software Merchant Wholesalers
          Computer and software stores
          Software publishers (except video game publishers)
          Computer, computer peripheral and pre-packaged software merchant wholesalers
          Residential building construction
          Other telecommunications
          Communication Equipment Repair and Maintenance
          Telecommunications Resellers
          All Other Telecommunications
          Materials testing
          Computer software
          Mathematics
          Computer-assisted image analysis (Medicine)
          Quality control
          Descriptive statistics
          Artificial neural networks
          Digital image processing
          Automation
          Construction industry
          Algorithms
      keyword:
        brick defects
        computer vision
        image analysis
        quality control
        brick defects
        computer vision
        image analysis
        quality control
      ab: Nowadays, the development of cost‐effective, data‐driven technological processes using telecommunication technologies is essential. One of the focuses is on automating the process of evaluating the manufactured goods' quality. Vision‐based technology is now becoming increasingly used for monitoring purposes. Despite its advancements, computer vision technology has practical limitations. These include the physical characteristics of the measuring process, features specific to the technological procedures, and constraints related to software and mathematical algorithms. Among the cutting‐edge approaches, optical methods combined with neural network algorithms (NN) stand out. This significance is particularly evident because numerous industries continue to depend on manual defect identification methods, which are labour intensive, slow, and subject to human subjectivity. The article introduces a novel approach based on computer vision methods. It outlines an automated optical inspection system designed to detect defects in bricks on a transport belt during the production process. The article presents the processing algorithms used and discusses the results obtained.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: N
    holdings:
      @attributes:
        islocal: N