An Efficient Fabric Defect Prediction Based on Modular Neural Network Classifier with Alternative Hard C-Means Clustering

  • Authors

    • S Rathinavel
    • T Kannaianl
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17892
  • Fabric defect, textile industry, modular neural network, C-Means cluster, median filter, segmentation, classification.

  • Abstract

    In India, textile industry has been mainly focused because it increased the economy day by day. But, it has some problem in the field of quality control. At present, it is mainly solved visually through skilled workers. Though, due to the human errors and eye fatigue, the system reliability has been restricted. So, in this research has been focused automatic fabric defect detection scheme. Here, Modular Neural Network (MNN) is proposed for fabric defect detection and classification with low cost and high accurate rate via using image processing schemes in the woven fabrics. At first, the images are collected from the machine and then preprocessed by using Enhanced Directional Switching Median Filter (EDWF) to reduce the impulse and stationary noise. To attain high accurate prediction, the preprocessed image has been segmented by using Alternative Hard C-Means (AHCM) cluster. After clustering, the images are converted to binary image. Then, the first order features has been extracted from the image. The extracted features are given as input to MNN, which classifies the fabric defects. In MNN, the weight factors are calculated by using back propagation algorithm and generate the output. The simulation results show that the proposed MNN attained high accuracy rate of 96.7% when compared to existing Artificial Neural Network (ANN) than Support Vector Machine with Genetic Algorithm (SVM-GA) classification algorithms.

     

     

  • References

    1. [1] Sengottuvelan P, Wahi A & Shanmugam A, “Automatic fault analysis of textile fabric using imaging systemsâ€, Research Journal of Applied Sciences, Vol.3, (2008), pp.26–31.

      [2] Vikrant T & Gaurav S, “Automatic fabric fault detection using morphological operations on bit planeâ€, International Journal of Engineering Research & Technology, Vol.2, (2013), pp.856–861.

      [3] Hong Kong Productivity Council, Textile Handbook 2000, The Hong Kong Cotton Spinners Association, 2000.

      [4] Ngan HYT, Pang GKH & Yung NHC, “Automated fabric defect detection—a reviewâ€, Image and Vision Computing, Vol.29, No.7, (2011), pp.442–458.

      [5] Tuceryan M & Jain A, Texture Analysis, The Handbook of Pattern Recognition and Computer Vision, 2nd Edition, World Scientific Publishing Co., (1998), pp.207-248.

      [6] Chan C & Pang G, “Fabric defect detection by Fourier analysisâ€, Conference Record of the IEEE Industry Applications Conference, Thirty-Fourth IAS Annual Meeting, Vol.3, (1999), pp.1743-1750.

      [7] Mahajan PM, Kolhe SR & Patil PM, “A review of automatic fabric defect detection techniquesâ€, Advances in Computational Research, Vol.1, No.2, (2009), pp.18-29.

      [8] Chin RT & Harlow CA, “Automated visual inspection: a surveyâ€, IEEE Trans. Pattern Analysis & Machine Intelligence, Vol.4, No.6, (1982), pp.557–573.

      [9] Haralick RM, “Statistical and structural approaches to textureâ€, Proceedings of the IEEE, Vol.67, No.5, (1979), pp.786–804.

      [10] Kaur N & Dalal M, “Application of machine vision techniques in textile (fabric) quality analysisâ€, IOSR Journal of Engineering, Vol.2, No.4, (2012), pp.582-584.

      [11] Xie X, “A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniquesâ€, Electronic Letters on Computer Vision and Image Analysis, Vol.7, No.3,(2008), pp.1-22.

      [12] Abdellah H, Ahmed R & Slimane O, “Defect detection and identification in textile fabric by SVM methodâ€, IOSR Journal of Engineering (IOSRJEN), Vol.4, No.12,(2014), pp.69-77.

      [13] Eldeeb H, Mohy , Elbagoury T, Aboseda K, Shady E & Eldessouki M, “An automated fabric fault detection and classification system based on computer vision and soft computingâ€, ACC Journal, (2013).

      [14] Mohammed Sathik M, “Feature Extraction On Colored X-Ray Images By Bitplane Slicing Techniqueâ€, International Journal of Engineering Science and Technology, Vol.2, No.7, (2010), pp.2820-2824.

      [15] Latif-Amet A, Ertuzun A & Ercil A, “An efficient method for texture defect detection: sub-band domain co-occurrence matricesâ€, Image and Vision Computing, Vol.18, No.6, (2000), pp.543–553.

      [16] Kumar A & Pang GKH, “Fabric defect segmentation using
      multichannel blob detectorsâ€, Optical Engineering, Vol.39, No.12,
      (2000), pp.3176–3190.

      [17] Kumar A & Pang GKH, “Defect detection in textured materials using Gabor filtersâ€, IEEE Transactions on Industry Applications, Vol. 38, No.2, (2002), pp.425–440.

      [18] Hamid A, Alireza A & Esmaeil S, “Defect detection in textiles using morphological analysis of optimal Gabor waveletâ€, Proceedings of the International Conference on Computer and Automation Engineering, (2009), pp.26–30.

      [19] Li M & Staunton RC, “Optimum Gabor filter design and
      local binary patterns for texture segmentationâ€, Pattern Recognition Letters, Vol.29, No.5, (2008), pp.664–672.

      [20] Ozdemir S & Ercil A, “Markov random fields and Karhumen-Loeve transforms for defect inspection of textile productsâ€, Proceedings of the IEEE Conference on Emerging Technologies and Factory Automation, Vol.2, (1996), pp.697–703.

      [21] Campbell JG, Fraley C, Murtagh F & Rafery AE, “Linear flaw detection in woven textiles using model-based clusteringâ€, Pattern Recognition Letters, Vol.18, No.14, (1997), pp.1539–1548.

      [22] Huang CC & Chen IC, “Neural-fuzzy classification for fabric defectsâ€, Textile Research Journal, Vol.71, No.3, (2001), pp.220–224.

      [23] Kumar A, “Neural network based detection of local textile defectsâ€, Pattern Recognition, Vol.36, No.7, (2003), pp.1645–165.

      [24] Jing F, Li M, Zhang HJ & Zhang B, “Unsupervised image segmentation using local homogeneity analysisâ€, Proceedings of the IEEE International Symposium on Circuits and Systems, (2003), pp.II456–II459.

      [25] Anitha S & Radha V, “Preprocessing using Enhanced Median Filter for Defect Detection in 2D Fabric Imagesâ€, International Journal of Engineering and Technology (IJET), Vol.6, No.2, (2014), pp.707-717.

      [26] Zhong Z, Qi P, Guan M, Xia Y & Fu Y, “Fabric defect detection system based on digital image processingâ€, Computer Modelling & New Technologies, Vol.18, No.10,(2014), pp.263-270.

      [27] G, Abikhanova, A Ahmetbekova, E Bayat, A Donbaeva, G Burkitbay (2018). International motifs and plots in the Kazakh epics in China (on the materials of the Kazakh epics in China), Opción, Año 33, No. 85. 20-43.

      A Mukanbetkaliyev, S Amandykova, Y Zhambayev, Z Duskaziyeva, A Alimbetova (2018). The aspects of legal regulation on staffing of procuratorial authorities of the Russian Federation and the Republic of Kazakhstan Opción, Año 33. 187-216

  • Downloads

  • How to Cite

    Rathinavel, S., & Kannaianl, T. (2018). An Efficient Fabric Defect Prediction Based on Modular Neural Network Classifier with Alternative Hard C-Means Clustering. International Journal of Engineering & Technology, 7(3.27), 277-284. https://doi.org/10.14419/ijet.v7i3.27.17892

    Received date: 2018-08-19

    Accepted date: 2018-08-19

    Published date: 2018-08-15