Toward cognitive support for automated defect detection

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


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.


Minimum ratio Defect detection Visual inspection Cognitive automation 



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.


  1. 1.
    Grand View Research (2018) Cognitive computing market size to reach USD 49.36 billion by 2025. Accessed 11 Feb 2018
  2. 2.
    Zhang Y, Peng L, Sun Y, Lu H (2018) Editorial: Intelligent industrial IoT integration with cognitive computing. Mob Netw Appl 23:185–187CrossRefGoogle Scholar
  3. 3.
    CBR (2017) IBM brings cognitive assistant to factory for cutting down inspection time. Accessed 11 Feb 2018
  4. 4.
    Melkote AK (2016) The future of cognitive robotic process automation. Accessed 11 Feb 2018
  5. 5.
    Chen M, Herrera F, Hwang K (2018) Cognitive computing: architecture, technologies and intelligent applications. IEEE Access 6:19774–19783CrossRefGoogle Scholar
  6. 6.
    Wu Q, Member S, Ding G, Member S, Xu Y, Member S (2014) Cognitive internet of things: a new paradigm beyond connection. IEEE Internet Things J 1(2):129–143CrossRefGoogle Scholar
  7. 7.
    Bannat A et al (2011) Artificial cognition in production systems. IEEE Trans Autom Sci Eng 8(1):148–174CrossRefGoogle Scholar
  8. 8.
    Chen M, Tian Y, Fortino G, Zhang J, Humar I (2018) Cognitive internet of vehicles. Comput Commun 120(January):58–70CrossRefGoogle Scholar
  9. 9.
    Lapido YL et al (2015) Cognitive high speed defect detection and classification in MWIR images of laser welding. In: Proceedings of SPIE, p 9657Google Scholar
  10. 10.
    Chen M, Li W, Hao Y, Qian Y, Humar I (2018) Edge cognitive computing based smart healthcare system. Futur Gener Comput Syst 86:403–411CrossRefGoogle Scholar
  11. 11.
    Qian Y et al (2018) Secure enforcement in cognitive internet of vehicles. IEEE Internet Things J 5(2):1242–1250CrossRefGoogle Scholar
  12. 12.
    Hossain MS, Muhammad G, Al Qurishi M (2018) Verifying the images authenticity in Cognitive Internet of Things (CIoT)-oriented cyber physical system. Mob Netw Appl 23:239–250CrossRefGoogle Scholar
  13. 13.
    Hossain MS, Muhammad G (2019) Emotion recognition using deep learning approach from audio–visual emotional big data. Inf Fusion 49:69–78CrossRefGoogle Scholar
  14. 14.
    Hanbay K, Talu MF, Özgüven ÖF (2016) Fabric defect detection systems and methods: a systematic literature review. Opt Int J Light Electron Opt 127(24):11960–11973CrossRefGoogle Scholar
  15. 15.
    Karimi MH, Asemani D (2014) Surface defect detection in tiling industries using digital image processing methods: analysis and evaluation. ISA Trans 53(3):834–844CrossRefGoogle Scholar
  16. 16.
    Neogi N, Mohanta DK, Dutta PK (2014) Review of vision-based steel surface inspection systems. EURASIP J Image Video Process 2014(1):1–19CrossRefGoogle Scholar
  17. 17.
    Satorres Martínez S, Ortega Vázquez C, Gámez García J, Gómez Ortega J (2017) Quality inspection of machined metal parts using an image fusion technique. Meas J Int Meas Confed 111:374–383CrossRefGoogle Scholar
  18. 18.
    Shojaedini SV, Kasbgar Haghighi R, Kermani A (2017) A new method for defect detection in lumber images: optimising the energy model by an irregular parametric genetic approach. Int Wood Prod J 8(1):26–31CrossRefGoogle Scholar
  19. 19.
    Xie X (2008) A review of recent advances in surface defect detection using texture analysis techniques. Electron Lett Comput Vis Image Anal 7(3):1–22CrossRefGoogle Scholar
  20. 20.
    Kumar A (2008) Computer-vision-based fabric defect detection : a survey. IEEE Trans Ind Electron 55(1):348–363CrossRefGoogle Scholar
  21. 21.
    Schneider D, Merhof D (2015) Blind weave detection for woven fabrics. Pattern Anal Appl 18(3):725–737MathSciNetCrossRefGoogle Scholar
  22. 22.
    Hu G, Wang Q, Zhang G (2015) Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage. Appl Opt 54(10):2963–2980CrossRefGoogle Scholar
  23. 23.
    Zhu B, Liu J, Pan R, Gao W, Liu J (2015) Seam detection of in homogeneously textured fabrics based on wavelet transform. Text Res J 85(13):1381–1393CrossRefGoogle Scholar
  24. 24.
    Li P, Zhang H, Jing J, Li R, Zhao J (2015) Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method. J Text Inst 106(6):587–592CrossRefGoogle Scholar
  25. 25.
    Tolba AS (2011) Fast defect detection in homogeneous flat surface products. Expert Syst Appl 38(10):12339–12347CrossRefGoogle Scholar
  26. 26.
    Hu GH (2015) Automated defect detection in textured surfaces using optimal elliptical Gabor filters. Opt Int J Light Electron Opt 126(14):1331–1340CrossRefGoogle Scholar
  27. 27.
    Guo X, Tang C, Zhang H, Chang Z (2012) Automatic thresholding for defect detection. ICIC Express Lett 6(1):159–164Google Scholar
  28. 28.
    Tolba AS (2011) Neighborhood-preserving cross correlation for automated visual inspection of fine-structured textile fabrics. Text Res J 81(19):2033–2042CrossRefGoogle Scholar
  29. 29.
    Popescu D, Dobrescu R, Nicolae M (2007) Texture classification and defect detection by statistical features. NAUN Int J 1(1):79–84Google Scholar
  30. 30.
    Susan S, Sharma M (2017) Automatic texture defect detection using Gaussian mixture entropy modeling. Neurocomputing 239:232–237CrossRefGoogle Scholar
  31. 31.
    Cohen FS, Fan Z, Attali S (1991) Automated inspection of textile fabrics using textural models. IEEE Trans Pattern Anal Mach Intell 13(8):803–808CrossRefGoogle Scholar
  32. 32.
    Zhang R, Hu Y, Guo W, Zhang C (2009) Multi-scale Markov random field based fabric image segmentation associate with edge information. Int Symp Comput Intell Des 1(7):566–569Google Scholar
  33. 33.
    Serafim AFL (1992) Segmentation of natural images based on multiresolution pyramids linking of the parameters of an autoregressive rotation invariant model. Application to leather defects detection. Proc Int Conf Pattern Recognit 3(M1):41–44Google Scholar
  34. 34.
    Çelik HI, Dülger LC, Topalbekiroǧlu M (2014) Development of a machine vision system: real-time fabric defect detection and classification with neural networks. J Text Inst 105(6):575–585CrossRefGoogle Scholar
  35. 35.
    Çelik Hİ, Dülger LC, Topalbekiro M (2014) Fabric defect detection using linear filtering and morphological operations. Indian J Fibre Text Res 39(September):254–259Google Scholar
  36. 36.
    Xue-wu Z, Yan-qiong D, Yan-yun L, Ai-ye S, Rui-yu L (2011) Expert systems with applications a vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM. Expert Syst Appl 38(5):5930–5939CrossRefGoogle Scholar
  37. 37.
    Sugumaran VÃ, Ramachandran KI (2007) Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mech Syst Signal Process 21:2237–2247CrossRefGoogle Scholar
  38. 38.
    Naso D, Turchiano B, Member S, Pantaleo P (2005) A fuzzy-logic based optical sensor for online weld defect-detection. IEEE Trans Ind Inf 1(4):259–273CrossRefGoogle Scholar
  39. 39.
    Jasper W, Joines J, Brenzovich J (2016) Fabric defect detection using a genetic algorithm tuned wavelet filter. J Text Inst 96:43–54CrossRefGoogle Scholar
  40. 40.
    Yuen CWM, Wong WK, Qian SQ, Chan LK, Fung EHK (2009) A hybrid model using genetic algorithm and neural network for classifying garment defects. Expert Syst Appl 36(2):2037–2047CrossRefGoogle Scholar
  41. 41.
    Yapi D, Mejri M, Allili MS, Baaziz N (2015) A learning-based approach for automatic defect detection in textile images. IFAC Pap Online 28(3):2423–2428CrossRefGoogle Scholar
  42. 42.
    Ren R, Hung T, Tan KC (2018) A generic deep-learning-based approach for automated surface inspection. IEEE Trans Cybern 48(3):929–940CrossRefGoogle Scholar
  43. 43.
    Li Y, Zhao W, Pan J (2017) Deformable patterned fabric defect detection with fisher criterion-based deep learning. IEEE Trans Autom Sci Eng 14(2):1256–1264CrossRefGoogle Scholar
  44. 44.
    Jen Clark (2017) IBM Watson IoT: cognitive visual inspection, July 4, 2017. Accessed April 2018
  45. 45.
    Miao Y et al (2018) Green cognitive body sensor network: architecture, energy harvesting and smart clothing based applications. IEEE Sens J. Google Scholar
  46. 46.
    Jen Clark. Cognitive inspection: IBM visual insights, July 4, 2017. Accessed April 2018
  47. 47.
    Bin Huang G, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRefGoogle Scholar
  48. 48.
    Abbas M, Albadr A, Tiun S (2017) Extreme learning machine: a review. Int J Appl Eng Res ISSN 12(14):973–4562Google Scholar
  49. 49.
    Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529CrossRefGoogle Scholar
  50. 50.
    Eisner R, Poulin B, Szafron D, Lu P, Greiner R (2005) Improving protein function prediction using the hierarchical structure of the gene ontology. IEEE Comput Intell Bioinform Comput Biol 00:1–10Google Scholar
  51. 51.
    Sokolova M, Japkowicz N, Szpakowicz N (2006) Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: AI 2006: advances in artificial intelligence, pp 1015–1021Google Scholar
  52. 52.
    TILDA (1996) Textile defect image database. University of Freiburg, Germany. Accessed 10 Jan 2018
  53. 53.
    Tolba AS, Atwan A, Amanneddine N, Mutawa AM, Khan HA (2010) Defect detection in flat surface products using log-Gabor filters. Int J Hybrid Intell Syst 7:187–201CrossRefGoogle Scholar
  54. 54.
    Tolba AS (2012) A novel multiscale-multidirectional autocorrelation approach for defect detection in homogeneous flat surfaces. Mach Vis Appl 23:739–750CrossRefGoogle Scholar

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

Personalised recommendations