Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine

• Autorzy: Zhou Zhiyu , Wang Chao , Gao Xu , Zhu Zefei , Hu Xudong , Zheng Xiao , Jiang Likai

Identyfikatory

DOI

10.5604/01.3001.0012.7510

Warianty tytułu

PL

Wykrywanie defektów tkaniny i ich klasyfikacja poprzez zastosowanie maszyny uczącej się (ADE-RELM)

• Języki publikacji: EN

Abstrakty

EN

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.

PL

W celu opracowania automatycznego modelu wykrywania i klasyfikowania defektów tkanin, zaproponowano nowatorską technikę wykrywania i klasyfikowania opartą na zastosowaniu maszyny uczącej się (ADE-RELM). Proponowana metoda ma na celu wykrywanie powszechnych defektów, takich jak przerwana osnowa i wątek oraz zabrudzenia po oleju. Wyniki eksperymentalne pokazują, że w porównaniu z tradycyjną metodą filtrów Gabora, operacją morfologiczną i lokalnym wzorcem binarnym, proponowana w artykule metoda pozwala na precyzyjne zlokalizowanie defektów i osiąga wysoką skuteczność ich wykrywania.

Słowa kluczowe

EN

defect detection; multi-scale dictionary learning; regularisation extreme learning machine; adaptive differential evolution

PL

defekty tkaniny; skuteczność wykrywania defektów; maszyna ucząca się

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2019
• Tom: Vol. 27, no. 1 (133)
• Strony: 67--77
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Zhou Zhiyu

• Zhejiang Sci-Tech University, School of Information Science and Technology, Hangzhou 310018, China

autor

Wang Chao

• Zhejiang Sci-Tech University, School of Information Science and Technology, Hangzhou 310018, China

autor

Gao Xu

• Zhejiang Sci-Tech University, School of Information Science and Technology, Hangzhou 310018, China

autor

Zhu Zefei

• Zhejiang Sci-Tech University, College of Mechanical Engineering and Automation, Hangzhou, 310018, China

autor

Hu Xudong

• Zhejiang Sci-Tech University, Ministry of Education, The Research Centre of Modern Textile Machinery Technology, Hangzhou, 310018, China

autor

Zheng Xiao

• Anhui University of Technology, School of Computer Science, Maanshan 243002, China

autor

Jiang Likai

• Zhejiang Sci-Tech University, School of Information Science and Technology, Hangzhou 310018, China

Bibliografia

• 1. Zhu Q, Wu M, Li J, Deng D. Fabric de-c fect detection via small scale over-complete basis set. Textile Research Journal 2014; 84(15): 1634-1649.
• 2. Jing J, Fan X, Li P. Patterned fabric defect detection via convolutional matching pursuit dual-dictionary. Optical Engineering 2016; 55(5): 053109.  
• 3. Liu Z, Yan L, Li C. Fabric defect detection based on sparse representation of main local binary pattern. International Journal of Clothing Science and Technology 2017; 29(3): 282-293.  
• 4. Celik HI, Topalbekiroglu M, Dulge LC. Real-Time denim fabric inspection using image analysis. FIBRES & TEXTILES in Eastern Europe 2015; 23, 3(111): 85-90.  
• 5. Henry Y, Grantham K, Nelson H. Automated fabric defect detection – A review. Image and Vision Computing 2011; 29(7): 442-458.  
• 6. Hu G, Wang Q, Zhang G. Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage. Applied Optics 2015; 54(10): 2963- 2980.  
• 7. Malek A, Drean J, Bigue L. Optimization of automated online fabric inspection by fast Fourier transform (FFT) and cross-correlation. Textile Research Journal 2013; 83(3): 256-268.  
• 8. Lucia B, Giuseppe B, Pisana P, Elisa R, Andrea S, Paolo V. Automated defect detection in uniform and structured fabrics using Gabor filters and PCA. Journal of Visual Communication and Image Representation 2013; 24(7): 838-845.  
• 9. Hu G. Optimal ring Gabor filter design for texture defect detection using a simulated annealing algorithm. International Conference on Information Science, Electronic and Electrical Engineering (ISEEE), Sapporo, Japan, 2014; pp 860- 864.
• 10. Celik H, Canan L, Mehmet T. Fabric defect detection using linear filtering and morphological operations. Indian Journal of Fiber & Textile Research 2014; 39 (3): 254-259.
• 11. Raheja J, Kumar S, Chaudhary A. Fabric defect detection based on GLCM and Gabor filter: A comparison. Optik 2013; 124(23): 6469-6476.
• 12. Raheja J, Kumar S, Chaudhary A. Real time fabric defect detection system on an embedded DSP platform. Optik 2013; 124(21): 5280-5284.
• 13. Jing J, Zhang H, Wang J, Li P, Jia J. Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method. Journal of the Textile Institute 2013; 104 (1): 18-27.
• 14. Zhou J, Wang J, Bu H. Fabric defect detection using a hybrid and complementary fractal feature vector and fcm-based novelty detector. FIBRES & TEXTILES in Eastern Europe 2017; 25(6): 46-52.
• 15. Qu T, Zou L, Zhang Q, Chen X, Fan C. Defect detection on the fabric with complex texture via dual-scale over-complete dictionary. Journal of the Textile Institute 2016; 107(6): 743-756.
• 16. Zhou J, Wang J. Fabric defect detection using adaptive dictionaries. Textile Research Journal 2013; 83(17): 1846- 1859.
• 17. Zhang D, Liu P, Zhang K, Zhang H, Wang Q, Jing X. Class relatedness oriented-discriminative dictionary learning for multiclass image classification. Pattern Recognition 2016; 59: 168-175.
• 18. Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 2006; 54(11): 4311-4322.
• 19. Bazi Y. Differential evolution extreme learning machine for the classification of hyperspectral images. IEEE Geosciences and Remote Sensing Letters 2014; 11(6): 1066-1070.
• 20. Sarker A, Elsayed M, Tapabrata R. Differential evolution with dynamic parameters selection for optimization problems. IEEE Transactions on Evolutionary Computation 2014; 18 (5): 689-707.
• 21. Zhou Z, Gao X, Zhang J, Zhu Z, Hu X. A novel hybrid model using the rotation forest-based differential evolution online sequential extreme learning machine for illumination correction of dyed fabrics. Textile Research Journal 2018; DOI: 10.1177/0040517518764020.
• 22. Zhang K. Outlier-robust extreme learning machine for regression problems. Neurocomputing 2015; 151: 1519-1527.
• 23. Zhou Z, Chen J, Song Y, Zhu Z, Liu X. RFSEN-ELM: Selective ensemble of extreme learning machines using rotation forest for image classification. Neural Network World 2017; 27(5): 499-51

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).