AN AIRBAG FABRIC INSPECTION FRAMEWORK
DOI:
https://doi.org/10.59277/RRST-EE.2025.2.19Keywords:
Defect detection, Airbag, Quality, Gabor filterAbstract
The production process of airbags demands near-zero defects, from raw materials to final assembly. To minimize fabric flaws, we conducted a study on the maintenance schedule of looms using a custom-designed vision system at a leading global manufacturer. We identified and classified all observable defects in the resulting fabric based on the proposed framework, which utilizes wavelet transform and supports high-speed processing. The results were correlated with loom maintenance and monitored over a two-year period to reduce unforeseen errors. As a result, the company successfully rescheduled repairs, decreased defects, and minimized material loss.
References
(1) J.L. Bouchot, G. Stübl, B. Moser, A template matching approach based on the discrepancy norm for defect detection on regularly textured surfaces, Proc. SPIE, 8000 (2011).
(2) R. Nayak, R. Padhye, Automation in Garment Manufacturing, The Textile Institute Book Series, Woodhead Publishing (2017).
(3) A. Conci, C.B. Proença, A fractal image analysis system for fabric inspection based on box-counting method, Computer Networks and ISDN Systems, 30, pp. 1887–1895 (1998).
(4) A. Kumar, G. Pang, Fabric defect segmentation using multichannel blob detectors, Opt. Eng., 39, 12, pp. 3176–3190 (2000).
(5) A. Kumar, Computer vision-based fabric defect detection: a survey, IEEE Trans. Industrial Electronics, 55, 1, pp. 348-363 (2008).
(6) S.P. Dhanalakshmi, R.R.S. Kumari, A survey of fabric defect detection techniques, Int. J. Appl. Eng. Res. (2018).
(7) K.-L. Mak, P. Peng, Fabric defect detection using multi-level tuned-matched Gabor filters, J. Ind. Manag. Optim., 8, 2 (2012).
(8) J.L. Raheja, S. Kumar, A. Chaudhary, Fabric defect detection based on GLCM and Gabor filter: A comparison, Optik, 124, 23, pp. 6469–6474 (2013).
(9) Y. Li, W. Zhao, J. Pan, Deformable patterned fabric defect detection with Fisher criterion-based deep learning, IEEE Trans. Autom. Sci. Eng., 14, 2, pp. 1256–1264 (2017).
(10) N.T. Deotale, T.K. Sarode, Fabric defect detection adopting combined GLCM, Gabor wavelet features and random decision forest, 3D Res., 10, 1, 5 (2019).
(11) G. Vladimir, I. Evgen, N.L. Aung, Automatic detection and classification of weaving fabric defects based on digital image processing, 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 2218–2221 (2019).
(12) A. Şeker, Evaluation of fabric defect detection based on transfer learning with pre-trained AlexNet, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1–4 (2018).
(13) B. Wei, K. Hao, X.-S. Tang, L. Ren, Fabric defect detection based on Faster RCNN, Int. Conf. Artif. Intell. Textile Apparel, pp. 45–51 (2018).
(14) Y. Guo, X. Kang, J. Li, Y. Yang, Automatic fabric defect detection method using AC-YOLOv5, Electronics, 12, 13, 2950 (2023).
(15) K.S. Kumar, M.R. Bai, LSTM-based texture classification and defect detection in a fabric, Measurement: Sensors, 26, 100603 (2023).
(16) M. Wang, Z. Chen, P. Wang, H. Zhu, L. Fan, Aerostat airbag damage detection based on VMD decomposition and GenSVM, J. Phys.: Conf. Ser., 2173, 012056 (2022).
(17) A. Rasheed, B. Zafar, A. Rasheed, N. Ali, M. Sajid et al., Fabric defect detection using computer vision techniques: A comprehensive review, Math. Probl. Eng., 2020, 8189403 (2020).
(18) L. Li, Q. Li, Z. Liu, L. Xue, Effective fabric defect detection model for high-resolution images, Appl. Sci., 13, 18, 10500 (2023).
(19) T. Meeradevi, S. Sasikala, Automatic fabric defect detection in textile images using a LabVIEW-based multiclass classification approach, Multimed. Tools Appl., 83, pp. 65753–65772 (2024).
(20) L. Luo, C. Deng, Z. Wu, S. Wang, T. Ye, Automobile airbag defect detection algorithm based on improved Faster RCNN, 2021 Int. Conf. Comput. Eng. Artif. Intell. (ICCEAI), pp. 281-285 (2021).
(21) M. Priya, C. Ramakrishnan, S. Karthik, Fetal 3D-echo classification and segmentation using color and textural features for TR detection, Rev. Roum. Sci. Tech. – Électrotechn. et Énerg., 69, 1, pp. 109–114 (2024).
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