Research Article
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Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model

Year 2023, Volume: 12 Issue: 4, 63 - 68, 28.12.2023

Kazım Hanbay

https://doi.org/10.46810/tdfd.1327971

Abstract

Fabric defects cause both labor and raw material losses and energy costs. These undesirable situations negatively affect the competitiveness of companies in the textile sector. Traditionally, human-oriented quality control also has important limitations such as lack of attention and fatigue. Robust and efficient defect detection systems can be developed with image processing and artificial intelligence methods. This study proposes a deep learning-based method to detect and classify common fabric defects in circular knitting fabrics. The proposed method adds a fine-tuned mechanism to the MobileNetV2 deep learning model. The added fine-tuned mechanism is optimized to classify fabric defects. The proposed model has been tested on a fabric dataset containing circular knitting fabric defects. Obtained results showed that the proposed method produced desired results in fabric defect detection and classification.

Keywords

Fabric defect detection Deep learning Image processing Circular knitting.

References

  • Abouelela A, Abbas HM, Eldeeb H, Wahdan AA, Nassar SM. Automated vision system for localizing structural defects in textile fabrics. Pattern Recognit Lett 2005;26:1435–43.
  • Tajeripour F, Kabir E, Sheikhi A. Fabric Defect Detection Using Modified Local Binary Patterns. EURASIP J Adv Signal Process 2008 20081 2007;2008:1–12.
  • Li P, Zhang H, Jing J, Li R, Zhao J. Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method. 2014;106:587–92.
  • Huang Y, Xiang Z. RPDNet: Automatic Fabric Defect Detection Based on a Convolutional Neural Network and Repeated Pattern Analysis. Sensors 2022, Vol 22, Page 6226 2022;22:6226.
  • Raheja JL, Kumar S, Chaudhary A. Fabric defect detection based on GLCM and Gabor filter: A comparison. Optik (Stuttg) 2013;124:6469–74.
  • Hanbay K, Talu MF, Özgüven ÖF. Fabric defect detection systems and methods—A systematic literature review. Optik (Stuttg) 2016;127:11960–73.
  • Rasheed A, Zafar B, Rasheed A, Ali N, Sajid M, Dar SH, et al. Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review. Math Probl Eng 2020;2020.
  • Mak KL, Peng P, Yiu KFC. Fabric defect detection using morphological filters. Image Vis Comput 2009;27:1585–92.
  • Zhou J, Wang J. Unsupervised fabric defect segmentation using local patch approximation. 2016;107:800–9.
  • Bumrungkun P. Defect detection in textile fabrics with snake active contour and support vector machines. J Phys Conf Ser 2019;1195:012006.
  • Zhao S, Yin L, Zhang J, Wang J, Zhong R. Real-time fabric defect detection based on multi-scale convolutional neural network. IET Collab Intell Manuf 2020;2:189–96.
  • Alruwais N, Alabdulkreem E, Mahmood K, Marzouk R, Assiri M, Abdelmageed AA, et al. Hybrid mutation moth flame optimization with deep learning-based smart fabric defect detection. Comput Electr Eng 2023;108:108706.
  • Zhang J, Jing J, Lu P, Song S. Improved MobileNetV2-SSDLite for automatic fabric defect detection system based on cloud-edge computing. Measurement 2022;201:111665.
  • Anandan P, Sabeenian RS. Fabric defect detection using Discrete Curvelet Transform. Procedia Comput Sci 2018;133:1056–65.
  • Suryarasmi A, Chang CC, Akhmalia R, Marshallia M, Wang WJ, Liang D. FN-Net: A lightweight CNN-based architecture for fabric defect detection with adaptive threshold-based class determination. Displays 2022;73:102241.
  • Pourkaramdel Z, Fekri-Ershad S, Nanni L. Fabric defect detection based on completed local quartet patterns and majority decision algorithm. Expert Syst Appl 2022;198:116827.
  • Hanbay K. Detecting of Circular Knitting Fabric Defects Using VGG16 Architecture. Turkish J Nat Sci 2022;11:125–9.
  • Hatami Varjovi M, Fatih TALU M, Hanbay K. Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics. Turkish J Nat Sci 2022;11:160–5.
  • Yin YY, Li LQ. A lightweight algorithm for woven fabric defect detection. China Autom. Congr., Institute of Electrical and Electronics Engineers (IEEE); 2022, p. 1025–9.
  • ÇELİK Hİ, DÜLGER LC, ÖZTAŞ B, KERTMEN M, GÜLTEKİN E. A Novel Industrial Application of CNN Approach: Real Time Fabric Inspection and Defect Classification on Circular Knitting Machine. Tekst ve Konfeksiyon 2022;32:344–52.
  • Arıkan CO. Developing an intelligent automation and reporting system for fabric inspection machines. Tekst ve Konfeksiyon 2019;29:93–100.
  • Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: Inverted Residuals and Linear Bottlenecks n.d.
  • Shamrat FJM, Azam S, Karim A, Ahmed K, Bui FM, De Boer F. High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images. Comput Biol Med 2023;155:106646.
  • Hanbay K, Talu MF, Özgüve ÖF, Öztürk D. Real-time detection of knitting fabric defects using shearlet transform. Tekst ve Konfeksiyon 2019;29:1–10.
  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2015;2016-December:2818–26.
  • Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, p. 1251–8.

Year 2023, Volume: 12 Issue: 4, 63 - 68, 28.12.2023

Kazım Hanbay

https://doi.org/10.46810/tdfd.1327971

Abstract

References

  • Abouelela A, Abbas HM, Eldeeb H, Wahdan AA, Nassar SM. Automated vision system for localizing structural defects in textile fabrics. Pattern Recognit Lett 2005;26:1435–43.
  • Tajeripour F, Kabir E, Sheikhi A. Fabric Defect Detection Using Modified Local Binary Patterns. EURASIP J Adv Signal Process 2008 20081 2007;2008:1–12.
  • Li P, Zhang H, Jing J, Li R, Zhao J. Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method. 2014;106:587–92.
  • Huang Y, Xiang Z. RPDNet: Automatic Fabric Defect Detection Based on a Convolutional Neural Network and Repeated Pattern Analysis. Sensors 2022, Vol 22, Page 6226 2022;22:6226.
  • Raheja JL, Kumar S, Chaudhary A. Fabric defect detection based on GLCM and Gabor filter: A comparison. Optik (Stuttg) 2013;124:6469–74.
  • Hanbay K, Talu MF, Özgüven ÖF. Fabric defect detection systems and methods—A systematic literature review. Optik (Stuttg) 2016;127:11960–73.
  • Rasheed A, Zafar B, Rasheed A, Ali N, Sajid M, Dar SH, et al. Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review. Math Probl Eng 2020;2020.
  • Mak KL, Peng P, Yiu KFC. Fabric defect detection using morphological filters. Image Vis Comput 2009;27:1585–92.
  • Zhou J, Wang J. Unsupervised fabric defect segmentation using local patch approximation. 2016;107:800–9.
  • Bumrungkun P. Defect detection in textile fabrics with snake active contour and support vector machines. J Phys Conf Ser 2019;1195:012006.
  • Zhao S, Yin L, Zhang J, Wang J, Zhong R. Real-time fabric defect detection based on multi-scale convolutional neural network. IET Collab Intell Manuf 2020;2:189–96.
  • Alruwais N, Alabdulkreem E, Mahmood K, Marzouk R, Assiri M, Abdelmageed AA, et al. Hybrid mutation moth flame optimization with deep learning-based smart fabric defect detection. Comput Electr Eng 2023;108:108706.
  • Zhang J, Jing J, Lu P, Song S. Improved MobileNetV2-SSDLite for automatic fabric defect detection system based on cloud-edge computing. Measurement 2022;201:111665.
  • Anandan P, Sabeenian RS. Fabric defect detection using Discrete Curvelet Transform. Procedia Comput Sci 2018;133:1056–65.
  • Suryarasmi A, Chang CC, Akhmalia R, Marshallia M, Wang WJ, Liang D. FN-Net: A lightweight CNN-based architecture for fabric defect detection with adaptive threshold-based class determination. Displays 2022;73:102241.
  • Pourkaramdel Z, Fekri-Ershad S, Nanni L. Fabric defect detection based on completed local quartet patterns and majority decision algorithm. Expert Syst Appl 2022;198:116827.
  • Hanbay K. Detecting of Circular Knitting Fabric Defects Using VGG16 Architecture. Turkish J Nat Sci 2022;11:125–9.
  • Hatami Varjovi M, Fatih TALU M, Hanbay K. Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics. Turkish J Nat Sci 2022;11:160–5.
  • Yin YY, Li LQ. A lightweight algorithm for woven fabric defect detection. China Autom. Congr., Institute of Electrical and Electronics Engineers (IEEE); 2022, p. 1025–9.
  • ÇELİK Hİ, DÜLGER LC, ÖZTAŞ B, KERTMEN M, GÜLTEKİN E. A Novel Industrial Application of CNN Approach: Real Time Fabric Inspection and Defect Classification on Circular Knitting Machine. Tekst ve Konfeksiyon 2022;32:344–52.
  • Arıkan CO. Developing an intelligent automation and reporting system for fabric inspection machines. Tekst ve Konfeksiyon 2019;29:93–100.
  • Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: Inverted Residuals and Linear Bottlenecks n.d.
  • Shamrat FJM, Azam S, Karim A, Ahmed K, Bui FM, De Boer F. High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images. Comput Biol Med 2023;155:106646.
  • Hanbay K, Talu MF, Özgüve ÖF, Öztürk D. Real-time detection of knitting fabric defects using shearlet transform. Tekst ve Konfeksiyon 2019;29:1–10.
  • Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2015;2016-December:2818–26.
  • Chollet F. Xception: Deep Learning with Depthwise Separable Convolutions. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, p. 1251–8.
There are 26 citations in total.

Details

Primary Language English
Subjects Decision Support and Group Support Systems
Journal Section Articles
Authors

Kazım Hanbay 0000-0003-1374-1417

Early Pub Date December 28, 2023
Publication Date December 28, 2023
Published in Issue Year 2023 Volume: 12 Issue: 4

Cite

APA Hanbay, K. (2023). Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model. Türk Doğa Ve Fen Dergisi, 12(4), 63-68. https://doi.org/10.46810/tdfd.1327971
AMA Hanbay K. Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model. TJNS. December 2023;12(4):63-68. doi:10.46810/tdfd.1327971
Chicago Hanbay, Kazım. “Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model”. Türk Doğa Ve Fen Dergisi 12, no. 4 (December 2023): 63-68. https://doi.org/10.46810/tdfd.1327971.
EndNote Hanbay K (December 1, 2023) Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model. Türk Doğa ve Fen Dergisi 12 4 63–68.
IEEE K. Hanbay, “Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model”, TJNS, vol. 12, no. 4, pp. 63–68, 2023, doi: 10.46810/tdfd.1327971.
ISNAD Hanbay, Kazım. “Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model”. Türk Doğa ve Fen Dergisi 12/4 (December 2023), 63-68. https://doi.org/10.46810/tdfd.1327971.
JAMA Hanbay K. Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model. TJNS. 2023;12:63–68.
MLA Hanbay, Kazım. “Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model”. Türk Doğa Ve Fen Dergisi, vol. 12, no. 4, 2023, pp. 63-68, doi:10.46810/tdfd.1327971.
Vancouver Hanbay K. Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model. TJNS. 2023;12(4):63-8.
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