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A defect detection method based on sub-image statistical feature for texture surface

  • Harbin Institute of Technology Shenzhen
  • Shenzhen Engineering Lab of Industrial Robots and Systems
  • Guilin University of Electronic Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Aiming at automatic visual inspection of texture surface, a texture surface defect detection method is proposed based on statistical feature of subimage. The proposed method only uses a simple image feature, gray level difference of subimage without image enhancing to detect defects on texture surface directly, avoid the feature computation of high dimension space and the learning process of large numbers of defective and defect-free similar images, which is nonsupervised detection and improving algorithm efficiency. A variety of texture surfaces from industrial manufacture materials are chosen to conduct experiments. Detection time is about few seconds and accuracy is 93.6%. Experiment results prove the proposed method can online detect various texture surface defects effectively.

Original languageEnglish
Title of host publicationEighth International Conference on Digital Image Processing, ICDIP 2016
EditorsXudong Jiang, Charles M. Falco
PublisherSPIE
ISBN (Electronic)9781510605039
DOIs
StatePublished - 2016
Externally publishedYes
Event8th International Conference on Digital Image Processing, ICDIP 2016 - Chengu, China
Duration: 20 May 201623 May 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10033
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference8th International Conference on Digital Image Processing, ICDIP 2016
Country/TerritoryChina
CityChengu
Period20/05/1623/05/16

Keywords

  • Defect detection
  • Gray level difference of sub-image
  • Statistical feature
  • Texture surface

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