Deep Learning for DENIM Defects Detection

Author:

Hammami Khaled,Feki Imed,Abdessalem Saber Ben

Publisher

Springer Nature Singapore

Reference10 articles.

1. Dorrity, L., Vachtsevanos, G., Jasper, W., Real-Time Fabric Defect Detection and Control in Weaving Processes, In: National Textile Center, 1995, G94–2, 143–152 (70% detect by worker revistaindustriatextila.ro/)

2. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Computer. Screw. Pattern Recognition, Jun. 2015, p. 3431–3440.

3. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Proc. Int. Conf. Med. Image Computer. Comput.-Assist. Intervent., Cham, Switzerland, 2015, p. 234–241.

4. V. Badrinarayanan, A. Kendall, and R. Cipolla. (2015). “SegNet: A deep convolutional encoder-decoder architecture for image segmentation.” [Online]. Available: https://ieeexplore.ieee.org/document/7803544.

5. L. Wang, S. Guo, W. Huang, and Y. Qiao. (2015). “Places205- VGGNet models for scene recognition.” [Online]. Available: https://arxiv.org/abs/1508.01667.

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