A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern
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DOI: 10.1007/s10845-020-01607-9
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References listed on IDEAS
- Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
- Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
- Pedro Malaca & Luis F. Rocha & D. Gomes & João Silva & Germano Veiga, 2019. "Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 351-361, January.
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Cited by:
- Yilin Li & Chengbo Yi & Jianwen Feng & Jingyi Wang, 2022. "Event-Based Impulsive Control for Heterogeneous Neural Networks with Communication Delays," Mathematics, MDPI, vol. 10(24), pages 1-16, December.
- Zichen Bai & Junfeng Jing, 2024. "Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3315-3330, October.
- Jie Zhang & Pengpeng Yao & Hochung Wu & John H. Xin, 2023. "Automatic color pattern recognition of multispectral printed fabric images," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2747-2763, August.
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Keywords
Weave pattern recognition; Texture analysis; Computer vision; Multi-task learning; Convolutional neural network;All these keywords.
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