Automatic fabric defect detection in textile images using a labview based multiclass classification approach
Author:
Funder
DST-SSTP
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-18087-7.pdf
Reference26 articles.
1. Karlekar VV, Biradar MS, Bhangale KB (2015) Fabric defect detection using wavelet filter. In: 2015 International Conference on Computing Communication Control and Automation. ICCUBEA, Pune, India, pp 712–715. https://doi.org/10.1109/ICCUBEA.2015.145
2. Santhosh KK, Tamil SR, Uthaya KM, Jaya VP, Finney DS (2020) Defect detection in fabrics using modified CNN. Waffen-Und Kostumkunde Journal, XI(VI) 233–236. https://doi.org/10.11205/WJ.2020.V11I6.05.100937
3. Garg M, Dhiman G (2020) Deep convolution neural network approach for defect inspection of textured surfaces. J Inst Electron Comput 2:28–38. https://doi.org/10.33969/JIEC.2020.21003
4. Li H, Zhang H, Liu L, Zhong H, Wang Y, Jonathan Wu QM (2020) Integrating deformable convolution and pyramid network in Cascade R-CNN for fabric defect detection. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Toronto, ON, Canada, pp 3029–3036. https://doi.org/10.1109/SMC42975.2020.9282875
5. Das S, Wahi A, Keerthika S, Thulasiram N (2020) Defect Analysis of Textiles Using Artificial Neural Network. Curr Trends Fashion Technol Textile Eng 6(1):01–05. https://doi.org/10.19080/CTFTTE.2020.06.555677
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