Gray Fabric Defect Detection Based on Statistical Template Matching

Abstract

To address the high cost of online detection equipment and the low adaptability and accuracy of online detection models that are caused by uneven lighting, high noise, low contrast and so on, a block-based template matching method incorporating fabric texture characteristics is proposed.Firstly, the template image set is evenly divided into N groups of sub-templates at the same positions to mitigate the effects of image illumination, reduce the model computation, and enhance the detection speed, with all image blocks being preprocessed.Then, the feature value information is extracted from the processed set of subtemplates at the same position, extracting two gray-level cooccurrence matrix (GLCM) feature values for each image block.These two feature values are then fused to construct a matching template.The mean feature value of all image blocks at the same position is calculated and used as the threshold for template detection, enabling automatic selection of template thresholds for different positions.Finally, the feature values of the image blocks in the experimental set are traversed and matched with subtemplates at the same positions to obtain fabric defect detection results.The detection experiments are conducted on a platform that simulates a fabric weaving environment, using defective gray fabrics from a weaving factory as the detected objects.The outcomes demonstrate the efficacy of the proposed method in detecting defects in gray fabrics, the mitigation of the impact of uneven external lighting on detection outcomes, and the enhancement of detection accuracy and adaptability.

Keywords

defect detection / gray-level co-occurrence matrix (GLCM) / template matching / gray fabric / feature extraction / online detection

References

[1] HUANG Y Q, JING J F, WANG Z. Fabric defect segmentation method based on deep learning[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-15.

[2] YANG J C, WANG C G, JIANG B, et al. Visual perception enabled industry intelligence: state of the art, challenges and prospects[J]. IEEE Transactions on Industrial Informatics, 2021, 17(3): 2204-2219.

[3] TAJERIPOUR F, KABIR E, SHEIKHI A. Fabric defect detection using modified local binary patterns[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2008(1): 783898.

[4] CHO C S, CHUNG B M, Park M J. Development of real-time vision-based fabric inspection system[J]. IEEE Transactions on Industrial Electronics, 2005, 52 (4): 1073-1079.

[5] ZHU R H, XIN B J, DENG N, et al. Review of fabric defect detection based on computer vision[J]. Journal of Donghua University(English Edition), 2023, 40(1): 18-26.

[6] LI W Y, CHENG L D. New progress of fabric defect detection based on computer vision and image processing[J]. Journal of Textile Research, 2014, 35(3): 158-164. (in Chinese)

[7] XU W F, DAI M H, ZHU D, et al. Application progress of machine vision in fabric defect detection[J]. Cotton Textile Technology, 2021, 49(4): 80-84. (in Chinese)

[8] SCHNEIDER D, HOLTERMANN T, MERHOF D. A traverse inspection system for high precision visual on-loom fabric defect detection[J]. Machine Vision and Applications, 2014, 25 (6): 1585-1599.

[9] WU Y, GUO P Y, LIU Y P, et al. Fabric defect detection based on local texture image representation[J]. Journal of Donghua University (Natural Science), 2023, 49(6): 73-79.

[10] MAK K L, PENG P, LAU H Y K. A real-time computer vision system for detecting defects in textile fabrics[C]//2005 IEEE International Conference on Industrial Technology. New York: IEEE, 2005: 469-474.

[11] SCHNEIDER D, HOLTERMANN T, NEUMANN F, et al.A vision based system for high precision online fabric defect detection[C]//2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). New York: IEEE, 2012: 1494-1499.

[12] MA J Y, JIANG X Y, FAN A X, et al. Image matching from handcrafted to deep features: a survey[J]. International Journal of Computer Vision, 2021, 129(1): 23-79.

[13] SHI L, WANG J Y, SUN S W, et al. Development on monitoring system for stamping receiving line based on visual inspection technology[J]. Forging & Stamping Technology, 2023, 48(9): 184-189. (in Chinese)

[14] WANG S Y, QIAN G K, LI D, et al. Real-time template matching method for edge features[J]. Journal of South China University of Technology (Natural Science Edition), 2023, 51(9): 1-10.

[15] HAMDI A A, SAYED M S, FOUAD M M, et al. Fully automated approach for patterned fabric defect detection[C]//2016 Fourth International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC). New York: IEEE, 2016: 48-51.

[16] GUSTIAN D A, ROHMAH N L, SHIDIK G F, et al. Classification of troso fabric using SVMRBF multi-class method with GLCM and PCA feature extraction[C]//2019 International Seminar on Application for Technology of Information and Communication (iSemantic). New York: IEEE, 2019: 7-11.

[17] ZHAO Y, ZUO B Q. Analysis on application of machine vision in fabric defect detection[J]. Computer Engineering and Applications, 2020, 56(2): 11-17. (in Chinese)

[18] ZHAO S X, ZHANG J, WANG J L, et al. Fabric defect detection algorithm based on twostage deep transfer learning[J]. Journal of Mechanical Engineering, 2021, 57(17): 86-97. (in Chinese)

[19] ZHENG Y T, WANG C Q, CHEN L L, et al. Research progress of fabric image processing methods based on convolutional neural network[J]. Advanced Textile Technology, 2022, 30 (5): 1-11, 20. (in Chinese)

[20] YUAN L H, FU L, YANG Y, et al. Analysis of texture feature extracted by gray level cooccurrence matrix[J]. Journal of Computer Applications, 2009, 29 (4): 1018-1021. (in Chinese)

[21] PIZER S M, AMBURN E P, AUSTIN J D, et al. Adaptive histogram equalization and its variations[J]. Computer Vision, Graphics, and Image Processing, 1987, 39(3): 355-368.

[22] DU X L, ZHANG W, GU B B, et al. GLCMbased adaptive block compressed sensing method for image[J]. Computer Science, 2018, 45(8): 277-282. (in Chinese)

[23] GAO C C, HUI X W. GLCM-based texture feature extraction[J]. Computer Systems & Applications, 2010, 19 (6): 195-198. (in Chinese)

[24] WANG Z S, FAN G B. Texture recognition method of remote sensing landform image based on multi-scale semi-coupled convolutional sparse coding[J]. Computer Measurement & Control, 2024, 32(10): 284-290. (in Chinese)

[25] LI C Y, YU H Y. Defect detection of plaid fabric based on periodic segmentation template subtraction[J]. Cotton Textile Technology, 2023, 51(3): 22-29. (in Chinese)

[26] YE S T, YOU S Q, HAO C, et al. Fabric defect detection based on cascade RCNN and two step clustering method[J]. Cotton Textile Technology, 2022, 50(7): 24-29. (in Chinese)

[27] WU Z, LIU X X, ZHENG L X, et al. A fabric defect detection method based on gray level cooccurrence matrix feature image[J]. Cyber Security and Data Governance, 2015, 34(21): 47-50, 54. (in Chinese)

[28] YAN B C, PAN R R, ZHOU J, et al. Real-time detection of fabric defects based on use of improved Itti salient model[J]. Journal of Textile Research, 2023, 44 (7): 95-102. (in Chinese)

[29] MEI S Q, XIAO Z, CHEN J, et al. Improved the fusion of global attention mechanism and decoupling head[J]. International Journal of Information Retrieval Research, 2024, 14(1): 1-18.

[30] WANG B, LI M, LEI C L, et al. Research progress in fabric defect detection based on deep learning[J]. Journal of Textile Research, 2023, 44(1): 219-227. (in Chinese)