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Fabric defect detection and classification via deep learning-based improved Mask RCNN

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Abstract

Fabric defect detection has been successfully implemented in the quality quick response system for textile manufacturing automation. It is challenging to detect fabric defects automatically because of the complexity of images and the variety of patterns in textiles. This study presented a deep learning-based IM-RCNN for sequentially identifying image defects in patterned fabrics. Firstly, the images are gathered from the HKBU database and these images are denoised using a contrast-limited adaptive histogram equalization filter to eliminate the noise artifacts. Then, the Sobel edge detection algorithm is utilized to extract pertinent attention features from the pre-processed images. Lastly, the proposed improved Mask RCNN (IM-RCNN) is used for classifying defected fabric into six classes, namely Stain, Hole, Carrying, Knot, Broken end, and Netting multiple, based on the segmented region of the fabric. The dataset that can be evaluated using the true-positive rate and false-positive rate parameters yields a higher accuracy of 0.978 for the proposed improved Mask RCNN. The proposed IM-RCNN improves the overall accuracy of 6.45%, 1.66%, 4.70%, and 3.86% better than MobileNet-2, U-Net, LeNet-5, and DenseNet, respectively.

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Data sharing is not applicable to this article as no new data were created or analyzed in this Research.

References

  1. Liu, J., Wang, C., Su, H., Du, B., Tao, D.: Multistage GAN for fabric defect detection. IEEE Trans. Image Process. 29, 3388–3400 (2019). https://doi.org/10.1109/TIP.2019.2959741

    Article  ADS  Google Scholar 

  2. Jing, J., Wang, Z., Rätsch, M., Zhang, H.: Mobile-Unet: an efficient convolutional neural network for fabric defect detection. Text. Res. J. 92(1–2), 30–42 (2022). https://doi.org/10.1177/0040517520928604

    Article  CAS  Google Scholar 

  3. Ouyang, W., Xu, B., Hou, J., Yuan, X.: Fabric defect detection using activation layer embedded convolutional neural network. IEEE Access 7, 70130–70140 (2019). https://doi.org/10.1109/ACCESS.2019.2913620

    Article  Google Scholar 

  4. Jing, J., Zhuo, D., Zhang, H., Liang, Y., Zheng, M.: Fabric defect detection using the improved YOLOv3 model. J. Eng. Fibers Fabr. 15, 1558925020908268 (2020). https://doi.org/10.1177/1558925020908268

    Article  Google Scholar 

  5. Jing, J.F., Ma, H., Zhang, H.H.: Automatic fabric defect detection using a deep convolutional neural network. Color. Technol. 135(3), 213–223 (2019). https://doi.org/10.1111/cote.12394

    Article  CAS  Google Scholar 

  6. Huang, Y., Xiang, Z.: RPDNet: automatic fabric defect detection based on a convolutional neural network and repeated pattern analysis. Sensors 22(16), 6226 (2022). https://doi.org/10.3390/s22166226

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  7. Liu, Q., Wang, C., Li, Y., Gao, M., Li, J.: A fabric defect detection method based on deep learning. IEEE Access 10, 4284–4296 (2022). https://doi.org/10.1109/ACCESS.2021.3140118

    Article  CAS  Google Scholar 

  8. Lu, Z., Zhang, Y., Xu, H., Chen, H.: Fabric defect detection via a spatial cloze strategy. Text. Res. J. 93(7–8), 1612–1627 (2023). https://doi.org/10.1177/00405175221135205

    Article  CAS  Google Scholar 

  9. Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018). https://doi.org/10.3390/s18041064

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  10. Silvestre-Blanes, J., Albero, T., Miralles, I., Pérez-Llorens, R., Moreno, J.: A public fabric database for defect detection methods and results. Autex Res. J. 19(4), 363–374 (2019). https://doi.org/10.2478/aut-2019-0035

    Article  Google Scholar 

  11. Zhou, H., Chen, Y., Troendle, D., Jang, B.: One-class model for fabric defect detection. arXiv preprint arXiv:2204.09648 (2022). https://doi.org/10.48550/arXiv.2204.09648

  12. Talu, M.F., Hanbay, K., Varjovi, M.H.: CNN-based fabric defect detection system on loom fabric inspection. Text. Appar. 32(3), 208–219 (2022). https://doi.org/10.32710/tekstilvekonfeksiyon.1032529

    Article  Google Scholar 

  13. Chakraborty, S., Moore, M., Parrillo-Chapman, L.: Automatic defect detection for fabric printing using a deep convolutional neural network. Int. J. Fash. Des., Technol. Educ. 15(2), 142–157 (2022). https://doi.org/10.1080/17543266.2021.1925355

    Article  Google Scholar 

  14. Fang, B., Long, X., Sun, F., Liu, H., Zhang, S., Fang, C.: Tactile-based fabric defect detection using convolutional neural network with attention mechanism. IEEE Trans. Instrum. Meas. 71, 1–9 (2022)

    Google Scholar 

  15. Dakshina, D.S., Jayapriya, P., Kala, R.: Saree texture analysis and classification via deep learning framework. Int. J. Data Sci. Artifi. Intell. 01(01), 20–25 (2023)

    Google Scholar 

  16. Huang, Y., Jing, J., Wang, Z.: Fabric defect segmentation method based on deep learning. IEEE Trans. Instrum. Meas. 70, 1–15 (2021). https://doi.org/10.1109/TIM.2020.3047190

    Article  Google Scholar 

  17. Voronin, V., Sizyakin, R., Zhdanova, M., Semenishchev, E., Bezuglov, D., Zelemskii, A.: Automated visual inspection of fabric image using deep learning approach for defect detection. In Automated Visual Inspection and Machine Vision IV, vol.11787, pp. 174–180 (2021). https://doi.org/10.1117/12.2592872

  18. Rong-qiang, L., Ming-hui, L., Jia-chen, S., Yi-bin, L.: Fabric defect detection method based on improved U-Net. In Journal of Physics: Conference Series, vol. 1948(1), pp. 012160 (2021). https://doi.org/10.1088/1742-6596/1948/1/012160

  19. Jun, X., Wang, J., Zhou, J., Meng, S., Pan, R., Gao, W.: Fabric defect detection based on a deep convolutional neural network using a two-stage strategy. Text. Res. J. 91(1–2), 130–142 (2021). https://doi.org/10.1177/0040517520935984

    Article  CAS  Google Scholar 

  20. Wu, J., Le, J., Xiao, Z., Zhang, F., Geng, L., Liu, Y., Wang, W.: Automatic fabric defect detection using a wide-and-light network. Appl. Intell. 51(7), 4945–4961 (2021). https://doi.org/10.1007/s10489-020-02084-6

    Article  Google Scholar 

  21. Arora, P., Hanmandlu, M.: Detection of defects in fabrics using information set features in comparison with deep learning approaches. J. Text. Inst. 113(2), 266–272 (2022). https://doi.org/10.1080/00405000.2020.1870326

    Article  Google Scholar 

  22. Li, F., Li, F.: Bag of tricks for fabric defect detection based on cascade R-CNN. Text. Res. J. 91(5–6), 599–612 (2021). https://doi.org/10.1177/0040517520955229

    Article  CAS  Google Scholar 

  23. Shi, B., Liang, J., Di, L., Chen, C., Hou, Z.: Fabric defect detection via low-rank decomposition with gradient information and structured graph algorithm. Inf. Sci. 546, 608–626 (2021). https://doi.org/10.1016/j.ins.2020.08.100

    Article  MathSciNet  Google Scholar 

  24. Zhou, T., Zhang, J., Su, H., Zou, W., Zhang, B.: EDDs: a series of efficient defect detectors for fabric quality inspection. Measurement 172, 10888 (2021). https://doi.org/10.1016/j.measurement.2020.108885

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the reviewers for all of their careful, constructive, and insightful comments in relation to this work.

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  1. Department Electronics and Instrumentation Engineering, Erode Sengunthar Engineering College, Erode, Tamil Nadu, 638057, India

    G. Revathy

  2. Department Electronics and Communication Engineering, Erode Sengunthar Engineering College, Erode, Tamil Nadu, 638057, India

    R. Kalaivani

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  1. G. Revathy
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The authors confirm contribution to the paper as follows: GR and RK were involved in study conception and design, data collection, analysis and interpretation of results, and draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.

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Revathy, G., Kalaivani, R. Fabric defect detection and classification via deep learning-based improved Mask RCNN. SIViP 18, 2183–2193 (2024). https://doi.org/10.1007/s11760-023-02884-6

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