Fabric defect detection based on a deep convolutional neural network using a two-stage strategy
Source abstract
Abstract
With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting model, to recognize the type of the defect in the fabric. In brief, the proposed framework mainly consists of two steps, namely local defect prediction and global defect recognition. Experiments on the dataset have demonstrated the superior performance in fabric defect detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Optical measurement and interference techniques · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Eco-textiles, Ministry of Education, Jiangnan University, China
References (37)
Show source-provided references
1. 10.1109/TIE.1930.89647610.1109/TIE.1930.896476 ↗
2. 10.1109/28.99316410.1109/28.993164 ↗
3. 10.1177/004051750605390610.1177/0040517506053906 ↗
4. 10.1177/00405175950650010110.1177/004051759506500101 ↗
5. 10.1109/28.87127410.1109/28.871274 ↗
6. Kim S, Lee MH and Woo K-B. Wavelet analysis to fabric defects detection in weaving processes. In: proceedings of the IEEE international symposium on industrial electronics (ISIE'99) (Cat. No. 99TH8465) , Bled, Slovenia, 12–16 July 1999, pp.1406–1409. Washington, DC: IEEE.
7. Krizhevsky A, Sutskever I and Hinton G. ImageNet classification with deep convolutional neural networks. In: international conference on neural information processing systems , (eds F. Pereira, C.J.C. Burges, L. Bottou and K.Q. Weinberger), Lake Tahoe, Nevada, United States, 3–6 December 2012, pp.1097–1105. New York, United States: Curran Associates Inc.
8. 10.1038/nature1453910.1038/nature14539 ↗
9. 10.1016/j.neunet.2014.09.00310.1016/j.neunet.2014.09.003 ↗
10. 10.1561/200000003910.1561/2000000039 ↗
11. Mnih V · arXiv preprint arXiv:1312.5602 · 2013
12. 10.1016/j.patrec.2016.08.01610.1016/j.patrec.2016.08.016 ↗
13. 10.1007/s11263-018-1125-z10.1007/s11263-018-1125-z ↗
14. 10.1016/j.patcog.2017.10.03010.1016/j.patcog.2017.10.030 ↗
15. 10.1016/j.engappai.2008.05.00610.1016/j.engappai.2008.05.006 ↗
16. Mak K, Peng P and Lau H. Optimal morphological filter design for fabric defect detection. In: 2005 IEEE international conference on industrial technology , Hong Kong, China, 14–17 December 2005, pp.799–804. New York: IEEE.10.1109/ICIT.2005.1600745 ↗
17. 10.1016/j.imavis.2009.03.00710.1016/j.imavis.2009.03.007 ↗
18. 10.1515/aut-2015-000110.1515/aut-2015-0001 ↗
19. 10.1109/ACCESS.2018.286805910.1109/ACCESS.2018.2868059 ↗
20. Lambert G and Bock F. Wavelet methods for texture defect detection. In: proceedings of international conference on image processing , 1997, Santa Barbara, CA, USA, 26–29 October 1997, vol. 3, pp.201–204. Washington, DC: IEEE.10.1109/ICIP.1997.632054 ↗
21. 10.1016/S0031-3203(02)00017-110.1016/S0031-3203(02)00017-1 ↗
22. 10.1016/j.neucom.2017.01.03910.1016/j.neucom.2017.01.039 ↗
23. Xiaobo Y · J Text Res · 2013 · 4 · 026
24. 10.1109/TMM.2014.229883210.1109/TMM.2014.2298832 ↗
25. 10.1016/S0031-3203(03)00005-010.1016/S0031-3203(03)00005-0 ↗
26. 10.1177/00405175030730030710.1177/004051750307300307 ↗
27. 10.1109/ACCESS.2017.267594010.1109/ACCESS.2017.2675940 ↗
28. 10.1109/ACCESS.2019.291362010.1109/ACCESS.2019.2913620 ↗
29. 10.1109/ACCESS.2018.284105510.1109/ACCESS.2018.2841055 ↗
30. Szegedy C, et al. Going deeper with convolutions. In: proceedings of the IEEE conference on computer vision and pattern recognition , (eds K. Grauman and E.K. Miller and A.Torralba), Massachusetts, Boston, United States, 8-10 June 2015, pp.1–9. Washington, DC: IEEE.10.1109/CVPR.2015.7298594 ↗
31. Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: proceedings of the IEEE conference on computer vision and pattern recognition , Las Vegas, United States, 26 June–1 July 2016, pp.2818–2826. Washington, DC: IEEE.10.1109/CVPR.2016.308 ↗
32. Simonyan K · Comput Sci · 2014 · 1409
33. Ioffe S · arXiv preprint arXiv:1502.03167 · 2015
34. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: proceedings of the IEEE conference on computer vision and pattern recognition , Las Vegas, United States, 26 June–1 July 2016, pp.770–778. Washington, DC: IEEE.10.1109/CVPR.2016.90 ↗
35. 10.1016/j.neucom.2015.09.01110.1016/j.neucom.2015.09.011 ↗
36. 10.1177/004051751347845110.1177/0040517513478451 ↗
37. 10.1364/AO.54.00296310.1364/AO.54.002963 ↗