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Toward cognitive support for automated defect detection

  • Ehab Essa
  • M. Shamim Hossain
  • A. S. Tolba
  • Hazem M. Raafat
  • Samir Elmogy
  • Ghulam Muahmmad
Cognitive Computing for Intelligent Application and Service
  • 32 Downloads

Abstract

With the development of cognitive computing, machine learning techniques, and big data analytics, cognitive support is crucial for automated industrial production. The real-time automated visual inspection in industrial production is a challenging task. Speed and accuracy are crucial factors for the process of automating the defect detection. Many statistical and spectrum analysis approaches have been introduced; however, they suffer from high computational cost with average performance. This paper proposes a neighborhood-maintaining approach, which is based on the minimum ratio for fast and reliable inspection of industrial products. The minimum ratio between local neighborhood sliding windows is used as a similarity measure for localizing defection. Extreme learning machine is then adapted to classify surfaces to defect or normal. A defect detection accuracy on textile fabrics has achieved 98.07% with 91.29% sensitivity and 99.67% specificity. The minimum ratio shows highly discriminant power to distinguish between normal and abnormal surfaces. A defective region produces a smaller value of minimum ratio than that of a defect-free region. Experimental results show superior speed and accuracy performance over many existing defect detection methods.

Keywords

Minimum ratio Defect detection Visual inspection Cognitive automation 

Notes

Acknowledgements

This work was supported by the Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia, through the Vice Deanship of Scientific Research Chairs.

Compliance with ethical standards

Conflict of interest

The authors do not have any type of conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science, Faculty of Computers and InformationMansoura UniversityMansouraEgypt
  2. 2.Computer Science DepartmentKuwait UniversityKuwaitKuwait
  3. 3.Research Chair of Pervasive and Mobile Computing, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  4. 4.Department of Software Engineering, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  5. 5.Department of Computer Engineering, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  6. 6.Misr University of Science and Technology6th of October CityEgypt

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