Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection-Reference-Cited by-同舟云学术

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Mobile-Deeplab: a lightweight pixel segmentation-based method for fabric defect detection

Published:2023-09-20 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Bai Zichen,Jing Junfeng^ORCID

Funder

Innovation Capability Support Program of Shaanxi

National Natural Science Foundation of China

The Youth Innovation Team of Shaanxi Universities

Shaanxi Province Qin Chuangyuan “scientists +engineers” team

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-023-02205-1.pdf

Reference47 articles.

1. Alata, O., & Ramananjarasoa, C. (2005). Unsupervised textured image segmentation using 2-D quarter plane autoregressive model with four prediction supports. Pattern Recognition Letters, 26(8), 1069–1081. https://doi.org/10.1016/j.patrec.2004.10.002

2. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615

3. Barman, J., Wu, H. C., & Kuo, C. F. J. (2022). Development of a real-time home textile fabric defect inspection machine system for the textile industry. Textile Research Journal, 92(23–24), 4778–4788. https://doi.org/10.1177/00405175221111477

4. Cao, J., Yang, G., & Yang, X. (2020). A pixel-level segmentation convolutional neural network based on deep feature fusion for surface defect detection. IEEE Transactions on Instrumentation and Measurement, 70, 1–12. https://doi.org/10.1109/TIM.2020.3033726

5. Chang, X., Liu, W., Zhu, C., Zou, X., & Gui, G. (2022). Bilayer Markov random field method for detecting defects in patterned fabric. Journal of Circuits, Systems and Computers, 31(03), 2250058. https://doi.org/10.1142/S021812662250058X

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