Multiclass Defect Classification

Multiclass Defect Classification is a supervised learning task in computer vision that categorizes detected defects into multiple predefined, distinct classes based on their visual characteristics, distinguishing it from binary or single-class defect detection by enabling simultaneous identification of diverse defect types.

86

Publications

4.1K

Citations

275

Authors

151

Institutions

Publications per year

2017–2026

50

Authors

275

  1. MC
    Maoxiang Chu

    University of Science and Technology Liaoning, China

    9 publications · 337 citations

    9

    h-index

  2. RG
    Rongfen Gong

    University of Science and Technology Liaoning, China

    9 publications · 337 citations

    9

    h-index

  3. XL
    Xiaoping Liu

    Lakehead University, Canada

    3 publications · 99 citations

    3

    h-index

  4. YY
    Yonghui Yang

    Hefei University of Technology, China

    2 publications · 48 citations

    2

    h-index

  5. AW
    Anna Wang

    Northeastern University, China

    2 publications · 49 citations

    2

    h-index

1–5 of 275

Venues

  1. Expert Systems with Applications

    8 publications · 697 citations

    8

    h-index

  2. 5

    h-index

  3. NDT & E International

    3 publications · 116 citations

    3

    h-index

  4. Electronics

    2 publications · 174 citations

    2

    h-index

  5. Information and Software Technology

    2 publications · 99 citations

    2

    h-index

1–5